MICAI 2005: Advances in Artificial Intelligence

We propose a novel, high-level model of human learning and cognition, based on association forming. The model configures any input data stream featuring a high incidence of repetition into an association network whose node clusters represent data ‘concepts’. It relies on the hypothesis that, irrespective of the high parallelism of the neural structures involved in cognitive processes taking place in the brain cortex, the channel through which the information is conveyed from the real world environment to its final location (in whatever form of neural structure) can transmit only one data item per time unit. Several experiments are performed on the ability of the resulting system to reconstruct a given underlying ‘world graph’ of concepts and to form and eventually maintain a stable, long term core of memory that we call ‘semantic’ memory. The existence of discontinuous, first order phase transitions in the dynamics of the system is supported with experiments. Results on clustering and association are shown as well.

The development of Artificial Intelligence (AI) research has followed mainly two directions: the use of symbolic and connectionist (artificial neural networks) methods. These two approaches have been applied separately in the solution of problems that require tasks of knowledge acquisition and learning. We present the results of implementing a Neuro-Symbolic Hybrid System (NSHS) that allows unifying these two types of knowledge representation. For this, we have developed a compiler or translator of symbolic rules which takes as an input a group of rules of the type IF ... THEN..., converting them into a connectionist representation. Obtained the compiled artificial neural network this is used as an initial neural network in a learning process that will allow the “refinement” of the knowledge. To prove the refinement of the hybrid approach, we carried out a group of tests that show that it is possible to improve in a connectionist way the symbolic knowledge.

In this paper, we extend Moss and Parikh’s bi-modal language for knowledge and effort by an additional modality describing improvement. Like the source language, the new one too has a natural interpretaion in spatial contexts. The result of this is that concepts like the comparison of topologies can be captured within the framework of modal logic now. The main technical issue of the paper is a completeness theorem for the tri-modal system arising from the new language.

In this paper, we propose a new belief revision operator, together with a method of its calculation. Our formalization differs from most of the traditional approaches in two respects. Firstly, we formally distinguish between defeasible observations and indefeasible knowledge about the considered world. In particular, our operator is differently specified depending on whether an input formula is an observation or a piece of knowledge. Secondly, we assume that a new observation, but not a new piece of knowledge, describes exactly what a reasoning agent knows at the moment about the aspect of the world the observation concerns.

A powerful and useful approach for modeling knowledge and qualitative reasoning is the

Cognitive Map

. The background of Cognitive Maps is the research about learning environments carried out by Cognitive Psychology since the nineteenth century. Along the last thirty years, these underlying findings inspired the development of computational models to deal with causal phenomena. So, a Cognitive Map is a structure of concepts of a specific domain that are related through cause-effect relations with the aim to simulate behavior of dynamic systems. In spite of the short life of the causal Cognitive Maps, nowadays there are several branches of development that focus on qualitative, fuzzy and uncertain issues. With this platform wide spectra of applications have been developing in fields like game theory, information analysis and management sciences. Wherefore, with the purpose to promote the use of this kind of tool, in this work is surveyed three branches of Cognitive Maps; and it is outlined one application of the Cognitive Maps for the student modeling that shows a conceptual design of a project in progress.

Interval algebra of Allen [4] propose a set of relations which is particularly interesting on historical annotating tasks [1]. However, finding the feasible relations and consistent scenario has been shown to be NP-complete tasks for interval algebra networks [11,10]. For point algebra networks and a restricted class of interval algebra networks, some works propose efficient algorithms to resolve it. Nevertheless, these sets of relations (made of basic relation disjunctions) are not intuitive for describing historical scenarios. In this paper we propose a set of concrete relations for the annotator, and we formalize it in terms of temporal algebras. We then describe how our model can be matched with other ones to merge calculation efficiency and information suitability.

In this paper, we describe EventNet, a toolkit for inferring temporal relations between Commonsense events. It comprises 10,000 nodes and 30,000 temporal links mined from the Openmind Commonsense Knowledge Base. It enables applications to deduce "obvious" (to people) temporal relations between commonly occurring events, for example: First, you wake up, then you can leave the house in the morning. The temporal relation might be one of cause and effect, of action/goal or prerequisite relations, or simply that they tend to follow each other in a commonly occurring “script”. In addition, the algorithm has some built-in heuristics to infer when its information is not enough. It then finds semantically similar nodes to dynamically search the knowledge base. EventNet has been used in projects such as an intelligent kitchen, and in intelligent interfaces for consumer electronics devices.

This paper describes an ontology-mapping framework in the context of query answering (QA). In order to incorporate uncertainty inherent to the mapping process, the system uses the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology. Our approach is particularly fit for a query-answering scenario, where an answer needs to be created in real time that satisfies the query posed by the user.

Ontology merging is the process of creating a new ontology from two or more existing ontologies with overlapping parts. Currently, there are many domain areas in Computer Science interested in this topic. Federated Databases and Semantic Web are some of them. In this paper we introduce a three level approach that provides a semi-automatic method to ontology merging. It performs some tasks automatically and guides the user in performing other tasks for which his intervention is required.

One of the big challenges of the knowledge management is the active and intelligent dissemination of the know-how to users while executing their tasks, without bothering them with information that is too far from their competences or out of their interest fields. Delivering a new document to the concerned users consists basically on appreciating the semantic relevance of the content of this document (domain ontology) in relation with users’ competences (competences ontology). In this paper, we illustrate the importance, within a documentary dissemination service, of the integration of an ontologies system, essentially based on the domain and the competences, but also on the users, the documents, the processes and the enterprise. The maintenance of these ontologies and basically those related to documents, to domain and to competences is a crucial aspect for the system survival. So, we describe what role the texts analysis can play for the maintenance of these ontologies.

Knowledge and Information distribution, which is one of the main processes in Knowledge Management, is greatly affected by

power

explicit relations, as well as by implicit relations like

trust

. Making decisions about whether to deliver or not a specific piece of information to users on the basis of a rationally justified procedure under potentially conflicting policies for power and trust relations is indeed a challenging problem. In this paper we model power relations, as well as delegation and trust, in terms of an argumentation formalism, in such a way that a dialectical process works as a decision core, which is used in combination with the existing knowledge and an information distribution system. A detailed example is presented and an implementation reported.

This paper presents the pertinence of the use of the Answer Set Programming (ASP) formalism for developing a computational model of a software agent for Computer Supported Collaborative Learning (CSCL) environments. This analytic model is based on a representation of for agent’s beliefs about the learner and the domain, together with the corresponding inference system with the appropriate rules to derive new beliefs about the capabilities of the learner, and its use in order to support effective collaboration and maintain learning possibilities for the group members. The model provides a representation of the structural knowledge frontier and the social knowledge frontier of the learner, which are the components for the definition of the learner’s zone of proximal development (zpd). Based on the zpd of its learner the agent can propose her a learning task and maintain the zpd for the learner in the group. The complete code of the model is presented in the declarative language of DLV, a logic programming language for implementing ASP models.

It is shown in this paper a way of representing deductive systems using the situation calculus. The situation calculus is a family of first order languages with induction that allows the specification of evolving worlds and reasoning about them and has found a number of applications in AI. A method for the representation of formulae and of proofs is presented in which the induction axiom on states is used to represent structural induction on formulae and proofs. This paper’s formalizations are relevant for the purpose of meta reasoning and of automated or manual deduction in the context of situation calculus specifications. An example proof is given for the fact that no deductive system is complete for arbitrary situation calculus specifications (an expectable result).

#

k

SAT is a complex problem equivalent to calculate the cardinalities of the null sets of conjunctive forms consisting of clauses with an uniform length. Each such null set is the union of linear varieties of uniform dimension in the hypercube. Here we study the class of sets in the hypercube that can be realized as such null sets. We look toward to characterize their cardinalities and the number of ways that they can be expressed as unions of linear varieties of uniform dimension. Using combinatorial and graph theory argumentations, we give such characterizations for very extremal values of

k

, either when it is very small or close to the hypercube dimension, and of the number of clauses appearing in an instance, either of value 2, or big enough to get a contradiction.

In the Belief Change domain, Katsuno and Mendelzon have proposed a set of postulates that should be satisfied by update operators. In 1989, Forbus semantically defined an update operator that satisfies these postulates. In order to calculate the resulting belief base all models of the relevant belief bases must be known. This paper proposes to use the

prime implicants

and

prime implicates

normal forms to represent these bases. Using this representation, a syntactical and computationally cheaper version of Forbus belief update operator is defined and a new minimal distance is proposed. We claim that this minimal distance ensures a better commitment between the minimal change criterion and the belief update definition.

Based on the idea that the cut sets of fuzzy sets are indeed crisp, but facilitate a normative theory for formalizing fuzzy set theory, this paper introduces cut sets of the fuzzy concepts and fuzzy roles as atomic concepts and atomic roles to build

$\mathcal{EFALCH}$

, a new fuzzy extension of

$\mathcal{ALCH}$

. This paper gives the definition of syntax, semantics and knowledge base of

$\mathcal{EFALCH}$

and discusses the comparison among

$\mathcal{EFALCH}$

and other fuzzy extensions of

$\mathcal{ALCH}$

. In addition, this paper defines the acyclic TBox form of

$\mathcal{EFALCH}$

, presents sound and complete algorithms for reasoning tasks w.r.t acyclic TBox, and proves the complexity of them is PSPACE-complete.

In constraint programming, a priori choices statically determine strategies that are crucial for resolution performances. However, the effect of strategies is generally unpredictable. We propose to dynamically change strategies showing bad performances. When this is not enough to improve resolution, we introduce some meta-backtracks. Our goal is to get good performances without the know-how of experts. Some first experimental results show the effectiveness of our approach.

A major technique to address state explosion problem in model checking is abstraction. Predicate abstraction has been applied successfully to large software and now to hardware descriptions, such as Verilog. This paper evaluates the state-of-the-art AI techniques—constraint logic programming (CLP)—to improve the performance of predication abstraction of hardware designs, and compared it with the SAT-based predicate abstraction techniques. With CLP based techniques, we can model various constraints, such as bit, bit-vector and integer, in a uniform framework; we can also model the word-level constraints without flatting them into bit-level constraints as SAT-based method does. With these advantages, the computation of abstraction system can be more efficient than SAT-based techniques. We have implemented this method, and the experimental results have shown the promising improvements on the performance of predicate abstraction of hardware designs.

Grouping problems arise in many applications, and the aim is to partition a set

U

of items, into a collection of mutually disjoint subsets or groups. The objective of grouping is to optimize a cost function such as to minimize the number of groups. Problems in this category may come from many different domains such as graph coloring, bin packing, cutting stock, and scheduling. This investigation is related in particular to scheduling transportation events, modeled as a grouping problem, and with the objective to minimize the number of vehicles used and satisfying the customer demand. There is a set of events to be scheduled (items) into a set of vehicles (groups). Of course, there are constraints that forbid assigning all events to a single vehicle. Two different techniques are used in this work to tackle the problem: Grouping Genetic Algorithms and an algorithm based on the heuristic DJD widely used for solving bin packing problems. Both methods were adapted to the problem and compared to each other using a set of randomly generated problem instances designed to comply with real situations.

We introduce a simple approach to finding approximate solutions to combinatorial problems. This approach called the Radial Search (RS) uses the concept of rings which define the location and size of search areas around current good solutions. It iteratively modifies the radii of these rings, and generates new centres, in order to cover the search space. A concentration step corresponds to choosing a solution as the centre of a new ring. An expansion step corresponds to the exploration around a given centre by increasing and reducing the radius of the ring until a better solution than the centre is found. This dynamic process of concentration and expansion of the search is repeated until a stopping condition is met.

A detailed description of RS, a discussion of its similarities and differences with current known methods, and its performance on TSP and QAP problems are given.

Set-valued Information Systems(SVISs) are generalized forms of Crisp Information Systems(CISs) and common in practice. This paper defines a fuzzy inclusion relation in Fuzzy Set-valued Information Systems(FSVISs). By means of two parameters of inclusion degree

λ

1

and

λ

2

, we define the rough sets in FSVISs, which are used to approximate fuzzy concepts in FSVISs. Furthermore, in terms of the maximum elements in the lattice derived from the universe according to decision attributes, we present the definitions and measuring methods of decision rules in FSVISs. Some examples have been given for illustration.

Although undirected cycles in directed graphs of Bayesian belief networks have been thoroughly studied, little attention has so far been given to a systematic analysis of directed (feedback) cycles. In this paper we propose a way of looking at those cycles; namely, we suggest that a feedback cycle represents a family of probabilistic distributions rather than a single distribution (as a regular Bayesian belief network does). A non-empty family of distributions can be explicitly represented by an ideal of conjunctions with interval estimates on the probabilities of its elements. This ideal can serve as a probabilistic model of an expert’s uncertain knowledge pattern; such models are studied in the theory of algebraic Bayesian networks. The family of probabilistic distributions may also be empty; in this case, the probabilistic assignment over cycle nodes is inconsistent. We propose a simple way of explicating the probabilistic relationships an isolated directed cycle contains, give an algorithm (based on linear programming) of its consistency checking, and establish a lower bound of the complexity of this checking.

In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon.

Fuzzeval,

our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it receives. Fuzzeval employs an evaluation function that adjusts the membership functions linked to the linguistic variables employed in the knowledge base. The membership functions are aligned to the average crisp input that was successfully used in the past winning games. Fuzzeval mechanisms are adaptive and have the simplicity associated with fuzzy controllers. Our experiments show that Fuzzeval improves its performance up to 42% after a match of only one hundred backgammon games played against

Pubeval,

a strong intermediate level program.

In this paper, a new fuzzy logic-based approach to production scheduling in the presence of uncertain disruptions is presented. The approach is applied to a real-life problem of a pottery company where the uncertain disruption considered is glaze shortage. This disruption is defined by two parameters that are specified imprecisely: number of glaze shortage occurrences and glaze delivery time. They are modelled and combined using standard fuzzy sets and level 2 fuzzy sets, respectively. A predictive schedule is generated in such a way as to absorb the impact of the fuzzy glaze shortage disruption. The schedule performance measure used is makespan. Two measures of predictability are defined: the average deviation and the standard deviation of the completion time of the last job produced on each machine. In order to analyse the performance of the predictive schedule, a new simulation tool FPSSIM is developed and implemented. Various tests carried out show that the predictive schedules have good performance in the presence of uncertain disruptions.

In Multi-agent based simulation (MABS) systems, computational models are built as multi-agent systems (MAS). Replication of these models can contribute to improve the reliability of the results and understanding of the system. One of the main problems for facilitating replication is the lack of a simulation integrated environment that supports the whole research process from conceptual modeling to simulation implementation and analysis. We address this issue providing a high-level conceptual modeling abstraction for simulation development, including transformation tools that facilitate the implementation of simulations on different simulation platforms. In this way, agent-based simulation development process is driven by modeling, because users focus on conceptual modeling, while implementation code is generated automatically.

Communication among agents in swarm intelligent systems and more generally in multiagent systems, is crucial in order to coordinate agents’ activities so that a particular goal at the collective level is achieved. From an agent’s perspective, the problem consists in establishing communication policies that determine

what

,

when

, and

how

to communicate with others. In general, communication policies will depend on the nature of the problem being solved. This means that the solvability of problems by swarm intelligent systems depends, among other things, on the agents’ communication policies, and setting an incorrect set of policies into the agents may result in finding poor solutions or even in the unsolvability of problems. As a case study, this paper focus on the effects of letting agents use different communication policies in ant-based clustering algorithms. Our results show the effects of using different communication policies on the final outcome of these algorithms.

In most practical situations, agents need to continuously improve or repair their plans. In a multiagent system agents also need to coordinate their plans. Consequently, we need methods such that agents in a multiagent system can construct, coordinate, and repair their plans. In this paper we focus on the problem of coordinating plans without exchanging explicit information on dependencies, or having to construct a global set of constraints. Our approach is to combine a propositional plan repair algorithm for each agent with a blackboard that auctions subgoals on behalf of the agents. Both the details of a first construction and some initial experimental results are discussed.

In this paper we propose a framework for controlling and executing problem solving methods in a work-flow context. The framework is founded on an extension and scaling-up of the electronic institutions theory and the use of artificial intelligence techniques in a multi agent environment. We discuss electronic institutions’s theory extensions for enabling intelligent organizations using our approach. As a proof of concept of the proposal we present the prototype of a help-desk information system for assigning advisors and monitoring their performance using artificial intelligence techniques for automated reasoning.

This work describes an application of extended behavior networks to the control of an agent in the game Unreal Tournament. Extended Behavior Networks (EBNs) are a class of action selection architectures capable of selecting a good set of actions for complex agents situated in continuous and dynamic environments. They have been successfully applied to the Robocup, but never before used in computer games. We verify the quality of the action selection mechanism and its correctness in a series of experiments. Then we asses the performance of an agent using an EBN against a plain reactive agent with identical sensory-motor apparatus and against a totally different agent built around finite-state machines. We discuss the results of our experiments, point our future work and conclude that extended behavior networks are a good control mechanism for game agents.

The present document explores how air pollution can be assessed from a multiagent point of view. In order to do so, a traffic system was simulated using agents as a way to measure if air pollution levels go down when the traffic lights employ a multigent cooperative system that negotiates the green light duration of each traffic light, in order to minimize the time a car has to wait to be served in an intersection. The findings after running some experiments where lanes of each direction are congested incrementally showed, that using this technique, there is a significant decrease in air pollution over the simulated area which means that traffic lights controlled by the multiagent system do improve the levels of air pollution.

The conventional technique for scene reconstruction from stereo image pairs searches for the best single surface fitting identified correspondences between the the two images. Constraints on surface continuity, smoothness, and visibility (occlusions) are incorporated into a ‘cost’ – usually an

ad hoc

linear combination of signal similarity criteria, with empirically selected coefficients. An unsatisfactory feature of this approach is that matching accuracy is very sensitive to correct choice of these coefficients. Also, few real scenes have only one surface, so that the single surface assumption contributes to matching errors.

We propose a noise-driven paradigm for stereo matching that does not couple the matching process with choice of surfaces by imposing constraints in the matching step. We call our strategy ‘Concurrent Stereo Matching’ because the first step involves a high degree of parallelism (making real-time implementations possible using configurable hardware): rather than search for ‘best’ matches, it first identifies all 3D volumes that match within a criteria based on noise in the image. Starting in the foreground, these volumes are then examined and surfaces are selected which exhibit high signal similarity in both images. Local constraints on continuity and visibility – rather than global ones – are used to select surfaces from the candidates identified in the first step.

Object recognition under occlusions is an important problem in computer vision, not yet completely solved. In this note we describe a simple but effective technique for the recognition objects under occlusions. The proposal uses the most distinctive parts of the objects for their further detection. During training, the proposal, first detects the distinctive parts of each object. For each of these parts an invariant description in terms of invariants features is next computed. With these invariant descriptions a specially designed set of associative memories (AMs) is trained. During object detection, the proposal, first looks for the important parts of the objects by means of the already trained AM. The proposal is tested with a bank of images of real objects and compared with other similar reported techniques.

In this paper, a fully automatic, real-time system is proposed to recognize seven basic facial expressions (angry, disgust, fear, happiness, neutral, sadness and surprise). First, faces are located and normalized based on an illumination insensitive skin model and face segmentation; then, the Local Binary Patterns (LBP) techniques, which are invariant to monotonic grey level changes, are used for facial feature extraction; finally, the Linear Programming (LP) technique is employed to classify seven facial expressions. Theoretical analysis and experimental results show that the proposed system performs well in some degree of illumination changes and head rotations.

In this document we present an agent for people detection and tracking through stereo vision. The agent makes use of the active vision to perform the people tracking with a robotic head on which the vision system is installed. Initially, a map of the surrounding environment is created including its motionless characteristics. This map will later on be used to detect objects in motion, and to search people among them by using a face detector. Once a person has been spotted, the agent is capable of tracking them through the robotic head that allows the stereo system to rotate. In order to achieve a robust tracking we have used the Kalman filter. The agent focuses on the person at all times by framing their head and arms on the image. This task could be used by other agents that might need to analyze gestures and expressions of potential application users in order to facilitate the human-robot interaction.

This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques. The object recognition is accomplished using an Artificial Neural Network (ANN) architecture which receives a descriptive vector called CFD&POSE as the input. This vector represents an innovative methodology for classification and identification of pieces in robotic tasks, every stage of the methodology is described and the proposed algorithms explained. The vector compresses 3D object data from assembly parts and it is invariant to scale, rotation and orientation, and it also supports a wide range of illumination levels. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is demonstrated through experimental results.

This paper presents a technique for color image segmentation, product of the combination and improvement of a number of traditional approaches: Seed region growth, Threshold classification and level on detail in the analysis of demand. First, a set of precise color classes with variable threshold is defined based on sample data. A scanline algorithn uses color clases with a small threshold to extract an initial group of pixels. These pixels are passed to a region growth method, which performs segmentation using higher-threshold classes as homogeneity criterion to stop growth. This hybrid technique solves disadvantages from individual methods and keeps their strengths. Its advantages include a higher robustness to external noise and variable illumination, efficiency on image processing, and quality on region segmentation, outperforming the results of standalone implementations of individual techniques. In addition, the proposed approach sets a starting point for further improvements.

The recognition of images beyond basic image processing often relies on training an adaptive system using a set of samples from a desired type of images. The adaptive algorithm used in this research is a learning automata model called CLS (collective learning systems). Using CLS, we propose a hierarchy of collective learning layers to learn color and texture feature patterns of images to perform three basic tasks: recognition, classification and segmentation. The higher levels in the hierarchy perform recognition, while the lower levels perform image segmentation. At the various levels the hierarchy is able to classify images according to learned patterns. In order to test the approach we use three examples of images: a) Satellite images of celestial planets, b) FFT spectral images of audio signals and c) family pictures for human skin recognition. By studying the multi-dimensional histogram of the selected images at each level we are able to determine the appropriate set of color and texture features to be used as input to a hierarchy of adaptive CLS to perform recognition and segmentation. Using the system in the proposed hierarchical manner, we obtained promising results that compare favorably with other AI approaches such as Neural Networks or Genetic Algorithms.

“To understand is to perceive patterns”

Sir Isaiah Berlin

(1909-1997)

In this paper we present the performance evaluation of a texture segmentation approach for synthetic textured images. Our segmentation approach uses a Bayesian inference procedure using co-ocurrence properties over a set of randomly sampled points in the image. We developed an exhaustive performance test for this approach that compares segmentation results to the “ground truth” images under a varying number of sampled points, in the neighborhood of each pixel used to classify it in the test images. We show our preliminary results that let us to choose the optimal number of points to analyze in the neighborhood of each pixel to assign a texture label. This method can be easily applied to segment outdoor real textured images.

A new image retrieval method based on salient points extracted from DCT compressed domain is proposed in this paper. Using significant DCT coefficients, we provide a robust self-adaptive salient point extraction algorithm which is very robust to most of common image processing. Based on salient points, two local image features, color histogram and LBP histogram are computed to represent local properties of the image for retrieval. Our system reduces the amount of data to be processed and only needs to do partial decompression, so it can accelerate the work of image retrieval. The experimental results also demonstrate it improves performance both in retrieval efficiency and effectiveness.

The ISOMAP algorithm has recently emerged as a promising dimensionality reduction technique to reconstruct nonlinear low-dimensional manifolds from the data embedded in high-dimensional spaces, by which the high-dimensional data can be visualized nicely. One of its advantages is that only one parameter is required, i.e. the neighborhood size or K in the K nearest neighbors method, on which the success of the ISOMAP algorithm depends. However, it’s an open problem how to select a suitable neighborhood size. In this paper, we present an effective method to select a suitable neighborhood size, which is much less time-consuming than the straightforward method with the residual variance, while yielding the same results. In addition, based on the characteristics of the Euclidean distance metric, a faster Dijkstra-like shortest path algorithm is used in our method. Finally, our method can be verified by experimental results very well.

Kernel based methods (such as

k

-nearest neighbors classifiers) for AI tasks translate the classification problem into a proximity search problem, in a space that is usually very high dimensional. Unfortunately, no proximity search algorithm does well in high dimensions. An alternative to overcome this problem is the use of approximate and probabilistic algorithms, which trade time for accuracy.

In this paper we present a new probabilistic proximity search algorithm. Its main idea is to order a set of samples based on their distance to each element. It turns out that the closeness between the order produced by an element and that produced by the query is an excellent predictor of the relevance of the element to answer the query.

The performance of our method is unparalleled. For example, for a

full

128-dimensional dataset, it is enough to review 10% of the database to obtain 90% of the answers, and to review less than 1% to get 80% of the correct answers. The result is more impressive if we realize that a full 128-dimensional dataset may span thousands of dimensions of clustered data. Furthermore, the concept of proximity preserving order opens a totally new approach for both exact and approximated proximity searching.

We present a neurobiologically motivated model for an agent which generates a representation of its spacial environment by an active exploration. Our main objectives is the introduction of an action-selection mechanism based on the principle of self-reinforcement learning. We introduce the action-selection mechanism under the constraint that the agent receives only information an animal could receive too. Hence, we have to avoid all supervised learning methods which require a teacher. To solve this problem, we define a self-reinforcement signal as qualitative comparison between predicted an perceived stimulus of the agent. The self-reinforcement signal is used to construct internally a self-punishment function and the agent chooses its actions to minimize this function during learning. As a result it turns out that an active action-selection mechanism can improve the performance significantly if the problem to be learned becomes more difficult.

The accuracy of the rules produced by a concept learning system can be hindered by the presence of errors in the data. Although these errors are most commonly attributed to random noise, there also exist “ill-defined” attributes that are too general or too specific that can produce systematic classification errors. We present a computer program called Newton which uses the fact that ill-defined attributes create an ordered error pattern among the instances to compute hypotheses explaining the classification errors of a concept in terms of too general or too specific attributes. Extensive empirical testing shows that Newton identifies such attributes with a prediction rate over 95%.

Reduced Support Vector Machines (RSVM) was proposed as the alternate of standard support vector machines (SVM) in order to resolve the difficulty in the learning of nonlinear SVM for large data set problems. RSVM preselects a subset as support vectors and solves a smaller optimization problem, and it performs well with remarkable efficiency on training of SVM for large problem. All the training points of the subset will be support vectors, and more training points are selected into this subset results in high possibility to obtain RSVM with better generalization ability. So we first obtain the RSVM with more support vectors, and selects out training examples near classification hyper plane. Then only these training examples are used as training set to obtain a standard SVM with less support vector than that of RSVM. Computational results show that standard SVMs on the basis of RSVM have much less support vectors and perform equal generalization ability to that of RSVM.

Learning systems need to face several problems: incrementality, tracking concept drift, robustness to noise and recurring contexts in order to operate continuously. A method for on-line induction of decision trees motivated by the above requirements is presented. It uses the following strategy: creating a delayed window in every node for applying forgetting mechanisms; automatic modification of the delayed window; and constructive induction for identifying recurring contexts. The default configuration of the proposed approach has shown to be globally efficient, reactive, robust and problem-independent, which is suitable for problems with unknown dynamics. Notable results have been obtained when noise and concept drift are present.

When dealing with multi-class classification tasks, a popular and applicable way is to decompose the original problem into a set of binary subproblems. The most well-known decomposition strategy is

one-against-one

and the corresponding widely-used method to recombine the outputs of all binary classifiers is

pairwise coupling

(PWC). However PWC has an intrinsic shortcoming; many meaningless partial classification results contribute to the global prediction result. Moreira and Mayoraz suggested to tackle this problem by using

correcting classifiers

[4]. Though much better performance was obtained, their algorithm is simple and has some disadvantages. In this paper, we propose a novel algorithm which works in two steps: First the original

pairwise probabilities

are converted into a new set of

pairwise probabilities

, then

pairwise coupling

is employed to construct the global posterior probabilities. Employing support vector machines as binary classifiers, we perform investigation on several benchmark datasets. Experimental results show that our algorithm is effective and efficient.

The kernel function of support vector machine (SVM) is an important factor for the learning result of SVM. Based on the wavelet decomposition and conditions of the support vector kernel function, Littlewood-Paley wavelet kernel function for SVM is proposed. This function is a kind of orthonormal function, and it can simulate almost any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM. According to the wavelet kernel function and the regularization theory, Least squares Littlewood-Paley wavelet support vector machine (LS-LPWSVM) is proposed to simplify the process of LPWSVM. The LS-LPWSVM is then applied to the regression analysis and classifying. Experiment results show that the precision is improved by LS-LPWSVM, compared with LS-SVM whose kernel function is Gauss function.

This paper proposes a state transition (ST) model as a classifier and its generalization by the minimization. Different from previous works using statistical methods, tree-based classifiers and neural networks, we use a ST model which determines classes of strings. Though an initial ST model only accepts given strings, the minimum ST model can accepts various strings by the generalization. We use a minimization algorithm by Mixed-Integer Linear Programming (MILP) approach. The MILP approach guarantees a minimum solution. Experiment was done for the classification of pseudo-strings. Experimental results showed that the reduction ratio from an initial ST model to the minimal ST model becomes small, as the number of examples increases. However, a current MILP solver was not feasible for large scale ST models in our formalization.

Compilers are nowadays fundamental tools for the development of any kind of application. However, their task gets increasingly difficult due to the constant increase in the complexity of modern computer architecture, as well as to the increased requirements imposed upon programming languages by the great diversity of applications handled at present. In the compilation process several optimization problems must be solved, some of them belonging to the NP-Hard class. The quality of the solution found for these problems has direct impact over the quality of the generated object code. To solve them, compilers do it locally through naive heuristics which might consequently lead to solutions that are far from optimal. Knowing that metaheuristics methods have recently been used massively and successfully to solve combinatorial optimization problems, similar performance in the problems found in the compilation process can be expected beforehand. Following this line of reasoning, such problems are presented in this paper and the potential use of metaheuristics techniques to find their solutions is analyzed. A review is also made of the work that has been done in this field, and finally a proposal is made of the road that this development should follow.

In recent years, the importance of the construction of fuzzy models from measured data has increased. Nevertheless, the complexity of real-life process is characterized by nonlinear and non-stationary dynamics, leaving so much classical identification techniques out of choice. In this paper, we present a comparison of Support Vector Machines (SVMs) for density estimation (SVDE) and for regression (SVR), versus traditional techniques as Fuzzy C-means and Gustafson-Kessel (for clustering) and Least Mean Squares (for regression), in order to find the parameters of Takagi-Sugeno (TS) fuzzy models. We show the properties of the identification procedure in a waste-water treatment database.

The main objective of this paper is two folds. First is to assess some well-known linear and nonlinear techniques comparatively in modeling and forecasting financial time series with trend and seasonal patterns. Then to investigate the effect of pre-processing procedures, such as seasonal adjustment methods, to the improvement of the modeling capability of a nonlinear structure implemented as ANNs in comparison to the classical Box-Jenkins seasonal autoregressive integrated moving average (ARIMA) model, which is widely used as a linear statistical time series forecasting method. Furthermore, the effectiveness of seasonal adjustment procedures, i.e. direct or indirect adjustments, on the forecasting performance is evaluated. The Autocorrelation Function (ACF) plots are used to determine the correlation between lags due to seasonality, and to determine the number of input nodes that is also confirmed by trial-and-errors. The linear and nonlinear models mentioned above are applied to aggregate retail sales data, which carries strong trend and seasonal patterns. Although, the results without any pre-processing were in an acceptable interval, the overall forecasting performance of ANN was not better than that of the classical method. After employing the right seasonal adjustment procedure, ANN has outperformed its linear counterpart in out-of-sample forecasting. Consequently, it is confirmed that the modeling capability of ANN is improved significantly by using a pre-processing procedure. The results obtained from both ARIMA and ANNs based forecasting methodologies are analyzed and compared with Mann-Whitney statistical test.

Import of intelligent features to time series analysis including the possibility of operating with linguistic information, reasoning and replying on intelligent queries is the prospective direction of development of such systems. The paper proposes novel methods of perception based time series data mining using perceptual patterns, fuzzy rules and linguistic descriptions. The methods of perception based forecasting using perceptual trends and moving approximation (MAP) transform are discussed. The first method uses perception based function for modeling qualitative forecasting given by expert judgments. The second method uses MAP transform and measure of local trend associations for description of perceptual pattern corresponding to the region of forecasting. Finally, the method of generation of association rules for multivariate time series based on MAP and fuzzy trends is discussed. Multivariate time series are considered as description of system dynamics. In this case association rules can be considered as relationships between system elements additional to spatial, causal etc. relations existing in the system. The proposed methods are illustrated on examples of artificial and real time series.

This paper is concerned with redundancy detection and elimination in databases via the solution of a key equivalence problem. The approach is related to the hardening of soft databases method due to Cohen

et al.

, [4]. Here, the problem is described in graph theoretic terms. An appropriate optimization model is drawn and solved indirectly. This approach is shown to be effective. Computational results on test databases are included.

Using the geodesic distance metric instead of the Euclidean distance metric, ISOMAP can visualize the convex but intrinsically flat manifolds such as the swiss roll data set nicely. But it’s well known that ISOMAP performs well only when the data belong to a single well-sampled manifold, and fails when the data lie on disjoint manifolds or imperfect manifolds. Generally speaking, as the data points are farer from each other on the manifold, the approximation of the shortest path to the geodesic distance is worse, especially for imperfect manifolds, that is, long distances are approximated generally worse than short distances, which makes the classical MDS algorithm used in ISOMAP unsuitable and thus often leads to the overlapping or ”overclustering” of the data. To solve this problem, we improve the original ISOMAP algorithm by replacing the classical MDS algorithm with the Sammon’s mapping algorithm, which can limit the effects of generally worse-approximated long distances to a certain extent, and thus better visualization results are obtained. As a result, besides imperfect manifolds, intrinsically curved manifolds such as the fishbowl data set can also be visualized nicely. In addition, based on the characteristics of the Euclidean distance metric, a faster Dijkstra-like shortest path algorithm is used in our method. Finally, experimental results verify our method very well.

Conversational Case-Based Reasoning (CCBR) provides a mixed-initiative dialog for guiding users to refine their problem descriptions incrementally through a question-answering sequence. Most CCBR approaches assume that there is at most one discrete value on each feature. While a generalized case (GC), which has been proposed and used in traditional CBR processes, has multiple values on some features. Motivated by the conversational software component retrieval application, we focus on the problem of extending CCBR to support GCs in this paper. This problem is tackled from two aspects: similarity measuring and discriminative question ranking.

In this paper we proposed an approach to maintain large case library, which based on the idea that a large case library can be transformed to a compact one by using a set of case-specific weights. A linear programming technique is being used to obtain case-specific weights. By learning such local weights knowledge, many of redundant or similar cases can be removed from the original case library or stored in a secondary case library. This approach is useful for case library with a large number of redundant or similar cases and the retrieval efficiency is a real concern of the user. This method of maintaining case library from scratch, as proposed in this paper, consists of two main steps. First, a linear programming technique for learning case-specific weights is used to evaluate the importance of different features for each case. Second, a case selection strategy based on the concepts of case coverage and reachability is carried out to select representative cases. Furthermore, a case retrieval strategy of the compact case library we built is discussed. The effectiveness of the approach is demonstrated experimentally by using two sets of testing data, and the results are promising.

Recently proposed manifold learning algorithms, e.g. Isometric feature mapping (Isomap), Locally Linear Embedding (LLE), and Laplacian Eigenmaps, are based on minimizing the construction error for data description and visualization, but not optimal from classification viewpoint. A discriminant isometric feature mapping for face recognition is presented in this paper. In our method, the geodesic distances between data points are estimated by Floyd’s algorithm, and Kernel Fisher Discriminant is then utilized to achieve the discriminative nonlinear embedding. Prior to the estimation of geodesic distances, the neighborhood graph is constructed by incorporating class information. Experimental results on two face databases demonstrate that the proposed algorithm achieves lower error rate for face recognition.

This paper presents a Grey-Markov forecasting model for forecasting the electric power requirement in China. This method takes into account the general trend series and random fluctuations about this trend. It has the merits of both simplicity of application and high forecasting precision. This paper is based on historical data of the electric power requirement in China, and forecasts and analyzes the electric power requirement in China by the Grey–Markov forecasting model. The forecasting precisions of Grey-Markov forecasting model from 2002 to 2004 are 99.42%, 98.05% and 97.56%, and those of GM(1,1) grey forecasting model are 98.53%, 94.02% and 88.48%. It shows that the Grey-Markov forecasting models have higher precision than GM(1,1) grey forecasting model. The results provides scientific basis for the planned development of the electric power supply in China.

We present a new approach for process fault detection based on models generated by machine learning techniques. Our work is based on a residual generation scheme, where the output of a model for process normal behavior is compared against actual process values. The residuals indicate the presence of a fault. The model consists of a general statistical inference engine operating on discrete spaces. This model represents the maximum entropy joint probability mass function (pmf) consistent with arbitrary lower order probabilities. The joint pmf is a rich model that, once learned, allows one to address inference tasks, which can be used for prediction applications. In our case the model allows the one step-ahead prediction of process variable, given its past values. The relevant past values for the forecast model are selected by learning a causal structure with an algorithm to learn a discrete bayesian network. The parameters of the statistical engine are found by an approximate method proposed by Yan and Miller. We show the performance of the prediction models and their application in power systems fault detection.

Mutation operators play an important role in evolutionary programming. Several different mutation operators have been developed in the past decades. However, each mutation operator is only efficient in some type of problems, but fails in another one. In order to overcome the disadvantage, a possible solution is to use a mixed mutation strategy, which mixes various mutation operators. In this paper, an example of such strategies is introduced which employs five different mutation strategies: Gaussian, Cauchy, Levy, single-point and chaos mutations. It also combines with the technique of species conservation to prevent the evolutionary programming from being trapped in local optima. This mixed strategy has been tested on 21 benchmark functions. The simulation results show that the mixed mutation strategy is superior to any pure mutation strategy.

We propose a new version of a multiobjective coevolutionary algorithm. The main idea of the proposed approach is to concentrate the search effort on promising regions that arise during the evolutionary process as a product of a clustering mechanism applied on the set of decision variables corresponding to the known Pareto front. The proposed approach is validated using several test functions taken from the specialized literature and it is compared with respect to its previous version and another approach that is representative of the state-of-the-art in evolutionary multiobjective optimization.

In this paper the Minimum Linear Arrangement (MinLA) problem is studied within the framework of memetic algorithms (MA). A new dedicated recombination operator called Trajectory Crossover (TX) is introduced and its performance is compared with four previous crossover operators. It is shown that the TX crossover induces a better population diversity. The MA using TX is evaluated on a set of well-known benchmark instances and is compared with several state-of-art MinLA algorithms.

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

The particle swarm optimization algorithm is a kind of intelligent optimization algorithm. This algorithm is prone to be fettered by the local optimization solution when the particle’s velocity is small. This paper presents a novel particle swarm optimization algorithm named particle swarm optimization with opposite particles which is guaranteed to converge to the global optimization solution with probability one. And we also make the global convergence analysis. Finally, three function optimizations are simulated to show that the PSOOP is better and more efficient than the PSO with inertia weights.

We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two perturbation operators: “c-perturbation” and “m-perturbation”. The goal of these operators is to prevent premature convergence and the poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules, enhanced with a dynamic

ε

-tolerance approach applicable to equality constraints. PESO is compared and outperforms highly competitive algorithms representative of the state of the art.

We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. The results obtained are discussed and some conclusions are provided.

A novel memetic algorithm for multi-objective optimization problems is proposed in this paper. The uniqueness of the method is that it hybridizes scalarizing selection with Pareto selection for exploitation and exploration. For extending the spread of solutions as quickly and fully as possible, the scalarizing functions defined by a wide diversified set of weights are used to go through all regions in objective space in the first phase at each generation. In the second phase, for intensifying search ability and achieving global exploration, a grid-based method is used to discover the gaps on existing tradeoff surface, and a fuzzy local perturbation is employed to reproduce additional ”good” individuals in the missing areas. Both the exploitation and exploration are made dynamic and adaptive to online optimization conditions based on a function of progress ratio, ensuring better stability of the algorithm. Compared with several state-of-the-art approaches using the same set of multi-objective 0/1 knapsack problem instances, experiment results show that the proposed method perform better to some extent in terms of finding a near-Pareto front and well-extended nondominated set.

The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. As evident from the empirical results, none of the five considered techniques could find an optimal solution for all the four performance measures. Further the results obtained by these five techniques are combined using an ensemble and two well known Evolutionary Multiobjective Optimization (EMO) algorithms namely Non-dominated Sorting Genetic Algorithm II (NSGA II) and Pareto Archive Evolution Strategy (PAES)algorithms in order to obtain an optimal ensemble combination which could also optimize the four different performance measures (objectives). We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test data. Empirical results reveal that the resulting ensemble obtain the best results.

Feature weighting is a more and more important step in clustering because data become more and more complex.

An embedded local feature weighting method has been proposed in [1].

In this paper, we present a new method based on the same cost function, but performed through a genetic algorithm. The learning process can be performed through an evolutionary approach or through a cooperavive coevolutionary approach. Moreover, the genetic algorithm can be combined with the original Weighting

K

-means algorithm in a Lamarckian learning paradigm.

We compare hill-climbing optimization versus genetic algorithms, evolutionary versus coevolutionary approaches, and Darwinian versus Lamarckian learning on different datasets.

The results seem to show that, on the datasets where the original algorithm is efficient, the proposed methods are even better.

David Goldberg has defined a

competent

genetic algorithm as one which “can solve hard problems, quickly, accurately, and reliably.” Among other

competent

genetic algorithms that have been developed are the Bayesian optimization algorithm (BOA), the fast messy genetic algorithm (fmGA), and the linkage learning genetic algorithm (LLGA). These algorithms have been tested on problems of bounded difficulty that are additive separable formed by deceptive subproblems of order not greater than

k

, where

k

< ℓ. BOA, fmGA, LLGA, and other

competent

genetic algorithms are stochastic, and thus, can only be assured of attaining optimality in a probabilistic sense. In this paper, we develop a deterministic algorithm that solves to optimality all linearly decomposable problems in a polynomial number of function evaluations with respect to the maximum size of the subproblems,

k

. The algorithm presented does not rely on a population, does not recombine individuals or apply any other

genetic

operator. Furthermore, because it is deterministic, the number of function evaluations required to find the optimum can be known in advance. The algorithm presented solves both the linkage and the optimization problems by finding the disjoint sets of related variables and the optimal values of these variables at the same time. The fact that such an algorithm can be devised has important implications for the design of GA-hard problems, and the development and evaluation of genetic optimization algorithms.

Neural maps are a very popular class of unsupervised neural networks that project high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid. It is desirable that the projection effectively preserves the structure of the data.

In this paper we present a hybrid model called K-Dynamical Self Organizing Maps (KDSOM) consisting of K Self Organizing Maps with the capability of growing and interacting with each other. The input space is soft partitioned by the lattice maps. The KDSOM automatically finds its structure and learns the topology of the input space clusters.

We apply our KDSOM model to three examples, two of which involve real world data obtained from a site containing benchmark data sets.

A back-propagation neural network is a large-scale dynamical system, most widely-used for scientific prediction. Its application was inhibited largely by the slow convergence rate and over-prolonged training time, primarily the results of inappropriate sample preprocessing for a large initial sample domain. To solve them, this paper introduced fuzzy clustering to scale-down the learning sample set, with representativeness of the whole sample domain. The established network was tested by analyzing the correlation coefficients between measured and predicted results in the least-square one-dimensional linear regression. In the application case, 50 subsamples were clustered out of the 250-sample domain of the S195 diesel engines to train the network of topological structure 8:9:2. The convergence rate was improved approximately 6.3 times. After validating the model by another untrained 10 samples, the correlation coefficients of the working power and the diesel consumption rate were respectively 0.968 and 0.986, indicating that the optimized network was applicable for data mining from a large knowledge pool.

Quantitatively examining determinants of foreign direct investment (FDI) in Central and East Europe (CEE) is an important research area. Traditional linear regression approaches have had difficulty in achieving conceptually and statistically reliable results. The key tasks addressed in this research are a neural network (NN) based (i) FDI forecasting model and (ii) nonlinear evaluation of the determinants of FDI. We have explored various modified backprop NN weights’ matrices and distinguished some nontraditional NN topologies. In terms of MSE and R-squared criteria, we found and checked relationship between modified NN input weights and FDI determinants weights. Results indicate that NN approaches better able to explain FDI determinants’ weights than traditional regression methodologies. Our findings are preliminary but offer important and novel implications for future research in this area including more detailed comparisons across sectors as well as countries over time.

The goal of this paper is to introduce an efficient predictive supervisory method for the trending of variables of technological processes and devices, with low run-time, for periodic analysis of high frequency, relatively (periods smaller than a second). This method allows to predict the time in which a process variable will arrive to an abnormal or important values. The data obtained in real time for each variable are used to estimate the parameters of a mathematical model. This model is continuous and of first-order or second-order (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the parameters. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

We link generative dependency grammars meeting natural modularity requirements with underspecified semantics of

Discourse Plans

intended to

account for exactly those meaning components that grammars of languages mark for

. We complete this link with a natural compilation of the modular dependency grammars into strongly equivalent efficiently analysed categorial dependency grammars.

The traditional English text chunking approach identifies phrases by using only one model and phrases with the same types of features. It has been shown that the limitations of using only one model are that: the use of the same types of features is not suitable for all phrases, and data sparseness may also result. In this paper, the Distributed Multi-Agent based architecture approach is proposed and applied in the identification of English phrases. This strategy put phrases into agents according to their sensitive features and identifies different phrases in parallel, where the main features are: one, easy and quick communication between phrases; two, avoidance of data sparseness. By applying and testing the approach on the public training and test corpus, the F score for arbitrary phrases identification using Distributed Multi-Agent strategy achieves 95.70% compared to the previous best F score of 94.17%.

Data sparseness problem is inherent and severe in language modeling. Smoothing techniques are usually widely used to solve this problem. However, traditional smoothing techniques are all based on statistical hypotheses without concerning about linguistic knowledge. This paper introduces semantic information into smoothing technique and proposes a similarity-based smoothing method which is based on both statistical hypothesis and linguistic hypothesis. An experiential iterative algorithm is presented to optimize system parameters. Experiment results prove that compared with traditional smoothing techniques, our method can greatly improve the performance of language model.

The paper discusses the usage of unlabeled data for Spanish Named Entity Recognition. Two techniques have been used: self-training for detecting the entities in the text and co-training for classifying these already detected entities. We introduce a new co-training algorithm, which applies voting techniques in order to decide which unlabeled example should be added into the training set at each iteration. A proposal for improving the performance of the detected entities has been made. A brief comparative study with already existing co-training algorithms is demonstrated.

The objective of this work is to automatically determine, in an unsupervised manner, Spanish prepositional phrases of the type preposition – nominal phrase – preposition (P(NP(P) that behave in a sentence as a lexical unit and their semantic and syntactic properties cannot be deduced from the corresponding properties of each simple form, e.g.,

por medio de

(by means of),

a fin de

(in order to),

con respecto a

(with respect to). We show that idiomatic P(NP(P combinations have some statistical properties distinct from those of usual idiomatic collocations. We also explore a way to differentiate P(NP(P combinations that could perform either as a regular prepositional phrase or as idiomatic prepositional phrase.

Syntactic links between content words in meaningful texts are intuitively conceived ‘normal,’ thus ensuring text cohesion. Nevertheless we are not aware on a broadly accepted Internet-based measure of cohesion between words syntactically linked in terms of Dependency Grammars. We propose to measure lexico-syntactic cohesion between content words by means of Internet with a specially introduced Stable Connection Index (

SCI)

.

SCI

is similar to Mutual Information known in statistics, but does not require iterative evaluation of total amount of Web-pages under search engine’s control and is insensitive to both fluctuations and slow growth of raw Web statistics. Based on Russian, Spanish, and English materials,

SCI

presented concentrated distributions for various types of word combinations; hence lexico-syntactic cohesion acquires a simple numeric measure. It is shown that

SCI

evaluations can be successfully used for semantic error detection and correction, as well as for information retrieval.

This paper presents how machine learning can be used to automatically obtain rules to divide words in Spanish into syllables. Machine learning is used in this case not only as a classifier to decide when a rule is used but to generate meaningful rules which then can be used to syllabify new words. Syllabification is an important task in speech recognition and synthesis since every syllable represents the sound in a single effort of articulation. Experiments were carried out using an Inductive Logic Programming (ILP) tool. The experiments were made on different sets of words to ascertain the importance of the number of examples in obtaining useful rules. The results show that it is possible to automatically obtain rules for syllabifying.

Question Classification (QC) is usually the first stage in a Question Answering system. This paper presents a multilingual SVM-based question classification system aiming to be language and domain independent. For this purpose, we use only surface text features. The system has been tested on the TREC QA track questions set obtaining encouraging results.

Passage Retrieval (PR) is typically used as the first step in current Question Answering (QA) systems. Most methods are based on the vector space model allowing the finding of relevant passages for general user needs, but failing on selecting pertinent passages for specific user questions. This paper describes a simple PR method specially suited for the QA task. This method considers the structure of the question, favoring the passages that contain the longer

n

-gram structures from the question. Experimental results of this method on Spanish, French and Italian show that this approach can be useful for multilingual question answering systems.

This paper studies the problem of building text classifiers using positive and unlabeled examples. The primary challenge of this problem as compared with classical text classification problem is that no labeled negative documents are available in the training example set. We call this problem PU-Oriented text Classification. Our text classifier adopts traditional two-step approach by making use of both positive and unlabeled examples. In the first step, we improved the 1-DNF algorithm by identifying much more reliable negative documents with very low error rate. In the second step, we build a set of classifiers by iteratively applying SVM algorithm on training data set, which is augmented during iteration. Different from previous PU-oriented text classification works, we adopt the weighted vote of all classifiers generated in the iteration steps to construct the final classifier instead of choosing one of the classifiers as the final classifier. Experimental results on the Reuter data set show that our method increases the performance (F1-measure) of classifier by 1.734 percent compared with PEBL.

We present a method for creating natural language interfaces to databases (NLIDB) that allow for translating natural language queries into SQL. The method is domain independent, i.e., it avoids the tedious process of configuring the NLIDB for a given domain. We automatically generate the domain dictionary for query translation using semantic metadata of the database. Our semantic representation of a query is a graph including information from database metadata. The query is translated taking into account the parts of speech of its words (obtained with some linguistic processing). Specifically, unlike most existing NLIDBs, we take seriously auxiliary words (prepositions and conjunctions) as set theory operators, which allows for processing more complex queries. Experimental results (conducted on two Spanish databases from different domains) show that treatment of auxiliary words improves correctness of translation by 12.1%. With the developed NLIDB 82of queries were correctly translated (and thus answered). Reconfiguring the NLIDB from one domain to the other took only ten minutes.

This paper presents an innovative hybrid approach for online recognition of handwritten symbols. The approach is composed of two main techniques. Firstly, fuzzy rules are used to extract a meaningful set of features from a handwritten symbol, and secondly a recurrent neural network uses the feature set as input to recognise the symbol. The extracted feature set is a set of basic shapes capturing what is distinctive about each symbol, thus making the network’s classification task easier. We propose a new recurrent neural network architecture, associated with an efficient learning algorithm derived from the gradient descent method. We describe the network and explain the relationship between the network and the Markov chains. The approach has achieved high recognition rates using benchmark datasets from the Unipen database.

In this paper, we describe environment compensation approach based on MAP (maximum a posteriori) estimation assuming that the noise can be modeled as a single Gaussian distribution. It employs the prior information of the noise to deal with environmental variabilities. The acoustic-distorted environment model in the cepstral domain is approximated by the truncated first-order vector Taylor series(VTS) expansion and the clean speech is trained by using Self-Organizing Map (SOM) neural network with the assumption that the speech can be well represented as the multivariate diagonal Gaussian mixtures model (GMM). With the reasonable environment model approximation and effective clustering for the clean model, the noise is well refined using batch-EM algorithm under MAP criterion. Experiment with large vocabulary speaker-independent continuous speech recognition shows that this approach achieves considerable improvement on recognition performance.

In this work, we propose to use as source of speech information the Short-MelfrequencyCepstra Time Transform (SMCTT),

c

τ

(

t

). The SMCTT studies the time properties at quefrency

τ

. Since the SMCTT signal,

c

τ

(

t

), comes from a nonlinear transformation of the speech signal,

s

(

t

), it makes the STMCTT a potential signal with new properties in time, frequency, quefrency, etc. The goal of this work is to present the performance of the SMCTT signal when the SMCTT is applied to an Automatic Speech Recognition (ASR) task. Our experiment results show that important information is given by this SMCTT waveform,

c

τ

(

t

).

In this paper we describe the work done to build a new voice based on diphone concatenation in the Spanish spoken in Mexico. This voice is compatible with the Text to Speech Synthesis System Festival. In the development of each module of the system the own features of Spanish were taken into account. In this work we hope to enhance the naturalness of the synthesized voice by including a prosodic model. The prosodic factors taken into consideration by the model are: phrasing, accentuation, duration and F0 contour. Duration and F0 prediction models were trained from natural speech corpora. We found the best prediction models by testing several machine learning methods and two different corpora. The paper describes the building, and training process as well as the results and their respective interpretation.

A method to represent arbitrary sequences (strings) is discussed. We emphasize the application of the method to the analysis of the similarity of sets of proteins expressed as sequences of amino acids. We define a pattern of arbitrary structure called a

metasymbol

. An implementation of a detailed representation is discussed. We show that a protein may be expressed as a collection of metasymbols in a way such that the underlying structural similarities are easier to identify.

Stochastic logic programs (SLPs) provide an efficient representation for complex tasks such as modelling metabolic pathways. In recent years, methods have been developed to perform parameter and structure learning in SLPs. These techniques have been applied for estimating rates of enzyme-catalyzed reactions with success. However there does not exist any method that can provide statistical inferences and compute confidence in the learned SLP models. We propose a novel approach for drawing such inferences and calculating confidence in the parameters on SLPs. Our methodology is based on the use of a popular technique, the bootstrap. We examine the applicability of the bootstrap for computing the confidence intervals for the estimated SLP parameters. In order to evaluate our methodology we concentrated on computation of confidence in the estimation of enzymatic reaction rates in amino acid pathway of Saccharomyces cerevisiae. Our results show that our bootstrap based methodology is useful in assessing the characteristics of the model and enables one to draw important statistical and biological inferences.

Multiple disorders are a daily problem in medical diagnosis and treatment, while most expert systems make an implicit assumption that only single disorder occurs in a single patient. In our paper, we show the need for performing multiple disorders diagnosis, and investigate a way of using inductive rules in our Case-based Reasoning System for diagnosing multiple disorder cases. We applied our approach to two medical casebases taken from real world applications demonstrating the promise of the research. The method also has the potential to be applied to other multiple fault domains, e.g. car failure diagnosis.

A Point Distribution Model (PDM) of the prostate has been constructed and used to automatically outline the contour of the gland in transurethral ultrasound images. We developed a new, two stages, method: first the PDM is fitted, using a multi-population genetic algorithm, to a binary image produced from Bayesian pixel classification. This contour is then used during the second stage to seed the initial population of a simple genetic algorithm, which adjusts the PDM to the prostate boundary on a grey level image. The method is able to find good approximations of the prostate boundary in a robust manner. The method and its results on 4 prostate images are reported.

An integrated method using the adaptive segmentation of brain tissues in Magnetic Resonance Imaging (MRI) images is proposed in this paper. Firstly, we give a template of brain to remove the extra-cranial tissues. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, result of classical watershed algorithm on gray-scale textured images such as tissue images is over-segmentation. The following procedure is a merging process for the over-segmentation regions using fuzzy clustering algorithm (Fuzzy C-Means). But there are still some regions which are not partitioned completely, particularly in the transitional regions between gray matter and white matter. So we proposed a rule-based re-segmentation processing approach to partition these regions. This integrated scheme yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.

In this paper we describe a human organ selection process in which agents argue over whether a given donor’s organ is viable for transplantation. This process is framed in the CARREL System; an agent-based organization designed to improve the overall transplant process. We formalize an argumentation based framework that enables CARREL agents to construct and assess arguments for and against the viability of a donor’s organ for a given potential recipient. We believe that the use of argumentation has the potential to increase the number of human organs that current selection processes make available for transplantation.

The market demand for dental implants is growing at a significant pace. In practice, some dental implants do not succeed. Important questions in this regard concern whether machine learning techniques could be used to predict whether an implant will be successful and which are the best techniques for this problem. This paper presents a comparative study on machine learning techniques for prediction of success of dental implants. The techniques compared here are: (a) constructive RBF neural networks (RBF-DDA), (b) support vector machines (SVM), (c) k nearest neighbors (kNN), and (d) a recently proposed technique, called NNSRM, which is based on kNN and the principle of structural risk minimization. We present a number of simulations using real-world data. The simulations were carried out using 10-fold cross-validation and the results show that the methods achieve comparable performance, yet NNSRM and RBF-DDA produced smaller classifiers.

This work presents an infant cry automatic recognizer development, with the objective of classifying three kinds of infant cries, normal, deaf and asphyxia from recently born babies. We use extraction of acoustic features such as LPC (Linear Predictive Coefficients) and MFCC (Mel Frequency Cepstral Coefficients) for the cry’s sound waves, and a genetic feature selection system combined with a feed forward input delay neural network, trained by adaptive learning rate back-propagation. We show a comparison between Principal Component Analysis and the proposed genetic feature selection system, to reduce the feature vectors. In this paper we describe the whole process; in which we include the acoustic features extraction, the hybrid system design, implementation, training and testing. We also show the results from some experiments, in which we improve the infant cry recognition up to 96.79% using our genetic system. We also show different features extractions that result on vectors that go from 145 up to 928 features, from cry segments of 1 and 3 seconds respectively.

A prospective paradigm, named Growing Functional Modules (GFM) has been introduced in a recent publication. Based on the epigenetic approach, the GFM paradigm is conceived to automatically generate "artificial brains" that are able to build, through interaction, their own representation of their environments. The present application consists in designing an artificial brain for a simple virtual mushroom shaped robot named hOnGo. This paper describes this initial implementation and its preliminary results.

In highly dynamic environments with uncertainty the elaboration of long or rigid plans is useless because the constructed plans are frequently dismissed by the arrival or new unexpected situations; in these cases, a “second-best” plan could rescue the situation. We present a new real-time planning method where we take into consideration the number and quality of future options of the next action to choose, in contrast to most planning methods that just take into account the intrinsic value of the chosen plan or the maximum valued future option. We apply our method to the Robocup simulated soccer competition, which is indeed highly dynamic and involves uncertainty. We propose a specific architecture for implementing this method in the context of a player agent in the Robocup competition, and we present experimental evidence showing the potential of our method.

This paper shows the performance of randomized low-discre-pancy sequences compared with others low-discrepancy sequences. We used two motion planning algorithms to test this performance: the expansive planner proposed in [1], [2] and SBL [3] . Previous research already showed that the use of deterministic sampling outperformed PRM approaches [4], [5], [6]. Experimental results show performance advantages when we use randomized Halton and Sobol sequences over Mersenne-Twister and the linear congruential generators used in random sampling.

We propose a framework for reactive motion and sensing planning based on critical events. A critical event amounts to crossing a critical curve, which divides the environment. We have applied our approach to two different problems: i) object finding and ii) pursuit-evasion. We claim that the proposed framework is in general useful for reactive motion planning based on information provided by sensors. We generalize and formalize the approach and suggest other possible applications.

For autonomous mobile robots following a planned path, self-localization is a very important task. Cumulative errors derived from the different noisy sensors make it absolutely necessary. Absolute robot localization is commonly made measuring relative distance from the robot to previously learnt landmarks on the environment. Landmarks could be interest points, colored objects, or rectangular regions as posters or emergency signs, which are very useful and not intrusive beacons in human environments. This paper presents an active localization method: a visual planning function selects from a free collision path and a set of planar landmarks, a subset of visible landmarks and the best combination of camera parameters (pan, tilt and zoom) for positions sampled along the path. A visibility measurement and some utility measurements were defined in order to select for each position, the camera modality and the subset of landmarks that maximize these local criteria. Finally, a dynamic programming method is proposed in order to minimize saccadic movements all over the trajectory.

A new reference walking trajectory for a planar five-link biped, considering both the SSP and the DSP, is presented firstly. A new combined controller to generate walking gaits following the reference trajectory is designed subsequently. The controller of five-link biped is consisted of PD controller and a fuzzy wavelet neural network controller. The scalable and shiftable coefficients of the wavelet function and weights of the network can be acquired by training the network by back-propagation algorithm online. The simulation results of the reference trajectory show that the proposed reference trajectory have good stability, repeatability and continuity during both SSP and the DSP, and when given the different initial conditions, the compatible trajectories can be achieved correspondingly. The simulation results of the trained controller show that the controller can generate the walking gaits to track the reference trajectory as close as possible.

Robotic end effectors are used over a diverse range of applications where they are required to grip with optimal force to avoid the object be either dropped or crushed. The slipping state can be easily detected by the output of the slip sensor. When the output has a non-zero value, the object is slipping. Conversely, detecting the deformation (crushing) state is more difficult, especially in an unstructured environment. Current proposed methodologies are

ad hoc

and specialised to the particular object or objects to be handled. Consequently, the gripper can only manipulate prior known objects, constraining the gripper application to a small set of predetermined objects. Accordingly, in this paper, it is proposed a hybrid approach of fuzzy and expert systems that permits to detect when an unknown object is being deformed. To determinate when the gripped object is being deformed, the fuzzy/expert system uses information from three sensors: applied force, slip rate and finger position. Several objects of different characteristics were used to prove the effectiveness of the proposed approach.

Advanced robotic systems require an end effector capable of achieving considerable gripping dexterity in unstructured environments. A dexterous end effector has to be able of dynamic adaptation to novel and unforeseen situation. Thus, it is vital that gripper controller is able to learn from its perception and experience of the environment. An attractive approach to solve this problem is intelligent control, which is a collection of complementary ’soft computing’ techniques within a framework of machine learning. Several attempts have been made to combine methodologies to provide a better framework for intelligent control, of which the most successful has probably been that of neurofuzzy modelling. Here, a neurofuzzy controller is trained using the actor-critic method. Further, an expert system is attached to the neurofuzzy system in order to provide the reward signal and failure signal. Results show that the proposed framework permits a transparent-robust control of a robotic end effector.

Intelligent service robots (ISRs) adapt to unpredictable environments by determining how to resolve the problems occurred in a troubled situation. As a way to successfully and continuously provide services, we envisage the software system embedded in a robot to dynamically reconfigure itself using new components selected from component repositories. This paper describes a component selection mechanism, which is an essential function to support such dynamic reconfiguration. We adopt a semantically-based component selection mechanism in which situational information around ISRs is represented as critical semantic information for the service robots to select software components.

Industrial manufacturing cells involving fixtureless environments require more efficient methods to achieve assembly tasks. This paper introduces an approach for Robotic Fixtureless Assembly (RFA). The approach is based on the Fuzzy ARTMAP neural network and learning strategies to acquire the skill from scratch without knowledge about the assembly system. The vision system provides the necessary information to accomplish the assembly task such as pose, orientation and type of component. Different ad-hoc input vectors were used as input to the assembly and the vision systems through several experiments which are described. The paper also describes the task knowledge acquisition and the followed strategies to solve the problem of automating the peg-in-hole assembly using 2D images. The approach is validated through experimental work using an industrial robot.

Robots can be greatly benefited from the integration of artificial senses in order to adapt to changing worlds. To be effective in complex unstructured environments robots have to perceive the environment and adapt accordingly. In this paper, it is introduced a biology inspired multimodal architecture called M

2

ARTMAP which is based on the biological model of sensorial perception and has been designed to be a more versatile alternative to data fusion techniques and non-modular neural architectures. Besides the computational overload compared to FuzzyARTMAP, M

2

ARTMAP reaches similar performance. This paper reports the results found in simulated environments and also the observed results during assembly operations using an industrial robot provided with vision and force sensing capabilities.

We present a remote operated robot application based on a standard CORBA distributed system to control a MOTOMAN 6-DOF arm manipulator. The robot is based on XRC2001 robot controller. Current approach could be extented to other Yaskawa controllers (e.g. ERC, ERCII, MRC, MRCII) without major changes. The main idea is to define a set of generic IDL interfaces that can be used to integrate commercial proprietary libraries hiding the intricate of low level components. The challenge is to create a remote client-server application which facilitates the integration of one or several arm manipulators based on mentioned controllers independently from computer system or different platforms.

Two communities work in parallel in model-based diagnosis: FDI and DX. In this work an integration of the FDI and the DX communities is proposed. Only relevant information for the identification of the minimal diagnosis is used. In the first step, the system is divided into clusters of components, and each cluster is separated into nodes. The minimal and necessary set of contexts is then obtained for each cluster. These two steps automatically reduce the computational complexity since only the essential contexts are generated. In the last step, a signature matrix and a set of rules are used in order to obtain the minimal diagnosis. The evaluation of the signature matrix is on-line, the rest of the process is totally off-line.

This paper presents a dynamic optimization approach based on the differential evolution (DE) strategy which is applied to the concurrent optimal design of a continuously variable transmission (CVT). The structure-control integration approach is used to state the concurrent optimal design as a dynamic optimization problem which is solved using the Constraint Handling Differential Evolution (CHDE) algorithm. The DE strategy is compared with the sequential approach. The results presented here demonstrate that the DE strategy is less expensive than the sequential approach from the computational implementation point of view.

This paper presents a new approach to improve performance in wireless ad-hoc networks with the DSR protocol using a Behavior-Based Architecture. Four levels of competence based on strategies to improve the cache were implemented for the Behavior-Based Architecture: sort routes, prefer fresher routes, selection, and disperse traffic. A conflict solver was implemented to solve counter level commands. Three different activation criteria for the conflict solver were developed, generating instances of the architecture. Two metrics were used to evaluate the performance of the ad-hoc network: end-to-end delay and dropped packet average for voice and data services, respectively. Six scenarios were analyzed showing the improvement of the network performance with respect to the original DSR protocol.

The main goal of this work is to study the reliability of fuzzy logic based systems. Three different configurations were compared to support this research. The context used was to guide a simulated robot through a virtual world populated with obstacles. In the first configuration, the system controls only the rotation angle of the robot. In the second one, there is an additional output that controls its step. In the third one, improvements were included in the decision process that controls the step and the rotation angle. In order to compare the performance of these approaches, we studied the controller stability based on the removal of rules. We measured two parameters: processing time and the amount of step necessary to reach the goal.

This research shows that simplicity and easiness of the design of fuzzy controllers don’t compromise its efficiency. Our experiments confirm that fuzzy logic based systems can properly perform under adverse conditions.

The features of a novel dynamical discrete-time algorithm for robust adaptive learning in feed-forward neural networks and its application to the neuro-adaptive nonlinear feedback control of systems with uncertain dynamics are presented. The proposed approach makes a direct use of variable structure systems theory. It establishes an inner sliding motion in terms of the neurocontroller parameters, leading the learning error toward zero. The outer sliding motion concerns the controlled nonlinear system, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. It is shown that there exists equivalence between the two sliding motions. The convergence of the proposed algorithm is established and the conditions are given. Results from a simulated neuro-adaptive control of Duffing oscillator are presented. They show that the implemented neurocontroller inherits some of the advantages of the variable structure systems: high speed of learning and robustness.

In this work a stable task space neuro algorithm for set-point control of robot manipulators with uncertain parameters is proposed. A depart from current approaches is the fact that a Wavelet Neural Network with on-line real-time learning seeks to explicitly compensate both the unknown gravity terms and the mismatch between the true and the estimated Jacobian matrix and the fact that it does not need velocity measurements. Linear position filtering is used to estimated the robot joint velocity in the control law and the properties of the Wavelet Neural Network are employed for avoiding velocity measurements in the learning rule. It is shown that all the closed loop signals are uniformly ultimately bounded. Experimental results in a two degrees of freedom robot are presented to evaluate the proposed controller.

Input-Output data modelling using fully tuned radial basis function networks(RBF) for a tilt rotor aircraft experimental platform is presented in this paper. The behavior of the four degree-of-freedom platform exemplifies a high order nonlinear system with significant cross-coupling between longitudinal, latitudinal directional motions, and tilt rotor nacelles rolling movement. This paper develops a practical algorithm coupled with model validity tests for identifying nonlinear autoregressive moving average model with exogenous inputs(NARMAX). It is proved that input-output data modelling using fully tuned algorithm is suitable for modelling novelty configuration air vehicles. A procedure for system modelling was proposed in the beginning of this paper and the subsequent sections provided detailed descriptions on how each stage in the procedure could be realized. The effectiveness of this modelling procedure is demonstrated through the tilt rotor aircraft platform. The estimated model can be utilized for nonlinear flight simulation and control studies.

This paper proposes a fuzzy logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude of the air vehicle are controlled. The implementation framework utilizes MATLAB’s standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, Microsoft Flight Simulator and FlightGear Flight Simulator are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.

Industrial CNC-milling centers demand adaptive control systems for better product quality. Surface roughness of machined parts is a key indicator of product quality, as it is closely related to functional features of parts such as fatigue life, friction, wear, etc. However, on-line control systems for surface roughness are not yet ready for industrial use. One of the main reasons is the absence of sensors that provide measurements reliably and effectively in a hostile machining environment. One potential solution is to combine readings from several different kinds of sensors in an intelligent sensor-fusion monitoring system. We implemented such a system and compared three modelling approaches for sensor-fusion: multiple regression, artificial neural networks (ANNs), and a new probabilistic approach. Probabilistic approaches are desirable because they can be extended beyond simple prediction to provide confidence estimates and diagnostic information as to probable causes. While our early experimental results with aluminum show that the ANN approach has the greatest predictive power over a variety of operating conditions, our probabilistic approach performs well enough to justify continued research given its many additional benefits.

We propose a general affective behavior model integrated to an intelligent tutoring system with the aim of providing the students with a suitable response from a pedagogical and affective point of view. The affective behavior model integrates the information from the student cognitive state, student affective state, and the tutorial situation, to decide the best pedagogical action. The affective model is implemented as a decision network with a utility measure on learning. For the construction of the affective behavior model, we are using personality questionnaires and emotions models. An initial evaluation of the model is presented, based on questionnaires applied to experienced teachers. We present the initial results of this evaluation.

Open learning environments often involve simulation where learners can experiment with different aspects and parameters of a given phenomenon to observe the effects of these changes. These are desirable in virtual laboratories. However, an important limitation of open learning environments is the effectiveness for learning, because it strongly depends on the learner ability to explore adequately. We have developed a semi-open learning environment for a virtual robotics laboratory based on simulation, to learn through free exploration, but with specific performance criteria that guide the learning process. We proposed a generic architecture for this environment, in which the key element is an intelligent tutoring system coupled to a virtual laboratory. The tutor module combines the performance and exploration behaviour of a student in several experiments, to decide the best way to guide his/her

.

We present an evaluation with an initial group of 20 students. The results show how this semi-open leraning environment can help to accelerate and improve the learning process.