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Über dieses Buch

This book constitutes the thoroughly refereed post-conference proceedings of the 20th International Conference on Case-Based Reasoning Research and Development (ICCBR 2012) held in Lyon, France, September 3-6, 2012. The 34 revised full papers presented were carefully selected from 51 submissions. The presentations and posters covered a wide range of CBR topics of interest to both practitioners and researchers, including foundational issues covering case representation, similarity, retrieval, and adaptation; conversational CBR recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning and scheduling systems.

Inhaltsverzeichnis

Frontmatter

Case-Based Reasoning and Expert Systems

Case-based reasoning (CBR) and expert systems have a long tradition in artificial intelligence: CBR since the late 1970s and expert systems since the late 1960s. While expert systems are based on expertise and expert reasoning capabilities for a specific area of responsibility, CBR is an approach for problem solving and learning of humans and computers. Starting from different research activities, CBR and expert systems have become overlapping research fields. In this talk the relationships between CBR and expert systems are analyzed from different perspectives like problem solving, learning, competence development, and knowledge types. As human case-based reasoners are quite successful in integrating problem-solving and learning, combining different problem solving strategies, utilizing different kinds of knowledge, and becoming experts for specific areas of responsibility, computer based expert systems do not have the reputation to be successful at these tasks. Based on this, the potential of CBR succeeding as future expert systems is discussed.

Klaus-Dieter Althoff

Reproducibility and Efficiency of Scientific Data Analysis: Scientific Workflows and Case-Based Reasoning

Scientists carry out complex scientific data analyses by managing and executing many related computational steps. Typically, scientists find a type of analysis relevant to their data, implement it step by step to try it out, and run many variants as they explore different datasets or method configurations. These processes are often done manually and are prone to error, slowing the pace of discoveries. Scientific workflows have emerged as a formalism to represent how the individual steps work and how they relate to the overall process. Workflows can be published, discovered, and reused to make data analysis processes more efficient through automation and assistance. In this talk, I will argue that integrating case-based reasoning techniques with workflows research would result in improved approaches to workflow sharing, retrieval, and adaptation. I will describe our initial work on semantic workflow matching using labeled graphs and knowledge intensive similarity measures. Furthermore, I will argue that if scientists followed a case-based approach more closely, scientific results would be more easily inspectable and reproducible. Through scientific workflows and case-based reasoning, scientific data analysis could be made more efficient and more rigorous.

Yolanda Gil

A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning

The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2

nd

order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.

Mobyen Uddin Ahmed, Peter Funk

Developing Case-Based Reasoning Applications Using myCBR 3

This paper presents the Open Source tool myCBR which has been re-implemented as standalone application with a designated application programming interface that can be used as plug-in for various applications. We will introduce how knowledge according to Richter’s knowledge containers can be modeled and how myCBR has been successfully applied within various applications. Especially we introduce novel features of myCBR that support knowledge engineers developing more comprehensive applications making use of existing knowledge such as Linked Data or User Generated Content. The applications presented in this paper present the high variety how CBR can be applied for web-based and mobile technologies as well as configuration, diagnostic or decision support tasks.

Kerstin Bach, Klaus-Dieter Althoff

Diverse Plan Generation by Plan Adaptation and by First-Principles Planning: A Comparative Study

Plan diversity has been explored in case-based planning, which relies on the availability of episodic knowledge, and in first-principles planning, which relies on the availability of a complete planning domain model. We present a first comparative study of these two approaches to obtaining diverse plans. We do so by developing a conceptual framework for plan diversity which subsumes both case-based and first-principles diverse plan generation, and using it to contrast two such systems, identifying their relative strengths and weaknesses. To corroborate our analysis, we perform a comparative experimental evaluation of these systems on a real-time strategy game domain.

Alexandra Coman, Héctor Muñoz-Avila

Case-Based Appraisal of Internet Domains

The increased economic importance of the Internet in all branches of industry has turned Internet domain names into “virtual properties” for electronic commerce. However, the appropriate appraisal of a domain name is a great challenge for trading partners. To address this problem, we propose a new approach derived from the sales comparison approach largely applied in the real estate market, which is implemented using case-based reasoning. The required case base that contains the experience in previous domain trade transactions is automatically extracted from the Internet. Based on carefully selected features derived from the domain name, cases are retrieved using a knowledge-intensive similarity measure. Further, case adaptation is performed to adjust the sales value of a previous domain transaction with respect to the difference to the target domain being assessed. The proposed method is implemented as a prototype Internet Domain Name Appraisal Tool (IDNAT). The performed empirical evaluation using 4,231 cases from the .de domain clearly demonstrates the feasibility of the proposed approach.

Sebastian Dieterle, Ralph Bergmann

Harnessing the Experience Web to Support User-Generated Product Reviews

Today, online reviews for products and services have become an important class of user-generated content and they play a valuable role for countless online businesses by helping to convert casual browsers into informed and satisfied buyers. In many respects, the content of user reviews is every bit as important as the catalog content that describes a given product or service. As users gravitate towards sites that offer insightful and objective reviews, the ability to source helpful reviews from a community of users is increasingly important. In this work we describe the Reviewer’s Assistant, a case-based reasoning inspired recommender system designed to help people to write more helpful reviews on sites such as Amazon and TripAdvisor. In particular, we describe two approaches to helping users during the review writing process and evaluate each as part of a blind live-user study. Our results point to high levels of user satisfaction and improved review quality compared to a control-set of Amazon reviews.

Ruihai Dong, Markus Schaal, Michael P. O’Mahony, Kevin McCarthy, Barry Smyth

Adapting Spatial and Temporal Cases

Qualitative algebras form a family of languages mainly used to represent knowledge depending on space or time. This paper proposes an approach to adapt cases represented in such an algebra. A spatial example in agronomy and a temporal example in cooking are given. The idea behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras.

Valmi Dufour-Lussier, Florence Le Ber, Jean Lieber, Laura Martin

eCo: Managing a Library of Reusable Behaviours

Building the behaviour for non-player characters in a game is a complex collaborative task among AI designers and programmers. In this paper we present a visual authoring tool for game designers that uses CBR techniques to support behaviour reuse. Our visual editor (called

eCo

) is capable of storing, indexing, retrieving and reusing behaviours previously designed by AI programmers. One of its most notable features is the

sketch-based retrieval

: that is, searching in a repository for behaviours that are similar to the one the user is drawing, and making suggestions about how to complete it. As this process relies on graph behaviour comparison, in this paper, we describe different algorithms for graph comparison, and demonstrate, through empirical evaluation in a particular test domain, that we can provide structure-based similarity for graphs that preserves behaviour similarity and can be computed at reasonable cost.

Gonzalo Flórez-Puga, Guillermo Jiménez-Díaz, Pedro A. González-Calero

Toward Measuring the Similarity of Complex Event Sequences in Real-Time

Traditional sequence similarity measures have a high time complexity and are therefore not suitable for real-time systems. In this paper, we analyze and discuss properties of sequences as a step toward developing more efficient similarity measures that can approximate the similarity of traditional sequence similarity measures. To explore our findings, we propose a method for encoding sequence information as a vector in order to exploit the advantageous performance of vector similarity measures. This method is based on the assumption that events closer to a point of interest, like the current time, are more important than those further away. Four experiments are performed on both synthetic and real-time data that show both disadvantages and advantages of the method.

Odd Erik Gundersen

Case-Based Project Scheduling

This paper presents a new approach for solving the Resource-Constrained Project Scheduling Problem using Case-Based Reasoning in a constructive way. Given a project to be scheduled our method retrieves similar projects scheduled in the past, selects the most similar project, and reuses as much as possible from the old solution to build a schedule for the project at hand. The result of this process is a partial schedule that is later extended and revised to produce a complete and valid schedule by a modified version of the Serial Schedule Generation Scheme. We present experimental results showing that our approach works well under reasonable assumptions. Finally, we describe several ways to modify our algorithm in the future so as to obtain even better results.

Mario Gómez, Enric Plaza

Adapting Numerical Representations of Lung Contours Using Case-Based Reasoning and Artificial Neural Networks

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of subjects. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the subject. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses Case-Based Reasoning principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the subject. This paper introduces the EquiVox platform and Artificial Neural Networks developed to interpolate the subject’s 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

Julien Henriet, Pierre-Emmanuel Leni, Rémy Laurent, Ana Roxin, Brigitte Chebel-Morello, Michel Salomon, Jad Farah, David Broggio, Didier Franck, Libor Makovicka

Adaptation in a CBR-Based Solver Portfolio for the Satisfiability Problem

The satisfiability problem was amongst the very first problems proven to be NP-Complete. It arises in many real world domains such as hardware verification, planning, scheduling, configuration and telecommunications. Recently, there has been growing interest in using portfolios of solvers for this problem. In this paper we present a case-based reasoning approach to SAT solving. A key challenge is the adaptation phase, which we focus on in some depth. We present a variety of adaptation approaches, some heuristic, and one that computes an optimal Kemeny ranking over solvers in our portfolio. Our evaluation over three large case bases of problem instances from artificial, hand-crafted and industrial domains, shows the power of a CBR approach, and the importance of the adaptation schemes used.

Barry Hurley, Barry O’Sullivan

A Local Rule-Based Attribute Weighting Scheme for a Case-Based Reasoning System for Radiotherapy Treatment Planning

This paper presents a novel local rule-based weighting scheme to determine attribute weights in a case-based reasoning system for radiotherapy treatment planning in brain cancer. A novel method of generating IF THEN rules to assign local weights to case attributes used in the nearest neighbour similarity measure is presented. The rules are prescreened using the data mining evaluation measures of confidence and support. Unique rules are then selected from the set of prescreened rules using an instance weighting algorithm that is based on a novel concept called the random retrieval probability of a training case, which is introduced to give an indication of the validity of the feedback obtained from a successful retrieval with respect to a particular training case. Experiments using real world brain cancer patient data show promising results.

Rupa Jagannathan, Sanja Petrovic

Learning and Reusing Goal-Specific Policies for Goal-Driven Autonomy

In certain adversarial environments, reinforcement learning (RL) techniques require a prohibitively large number of episodes to learn a high-performing strategy for action selection. For example, Q-learning is particularly slow to learn a policy to win complex strategy games. We propose GRL, the first GDA system capable of learning and reusing goal-specific policies. GRL is a case-based goal-driven autonomy (GDA) agent embedded in the RL cycle. GRL acquires and reuses cases that capture episodic knowledge about an agent’s (1) expectations, (2) goals to pursue when these expectations are not met, and (3) actions for achieving these goals in given states. Our hypothesis is that, unlike RL, GRL can rapidly fine-tune strategies by exploiting the episodic knowledge captured in its cases. We report performance gains versus a state-of-the-art GDA agent and an RL agent for challenging tasks in two real-time video game domains.

Ulit Jaidee, Héctor Muñoz-Avila, David W. Aha

Custom Accessibility-Based CCBR Question Selection by Ongoing User Classification

Question selection in Conversational Case-Based Reasoning (CCBR) is traditionally guided by the discriminativeness of questions, to minimize dialog length for retrieval. However, users may not always be able or willing to answer the most discriminative questions. This paper presents Accessibility Influenced Attribute Selection Plus (AIAS+), a method for customizing CCBR question selection to reflect the types of questions the user is likely to answer. Given background knowledge about response probabilities for different questions by different user groups, AIAS+ performs ongoing classification of new users, based on the questions they choose to answer, uses the classifications to predict the likelihood of the user answering particular questions, and applies those predictions to guide question selection. In addition, its question selection process balances questions’ information gain against their potential to aid user classification, to enable better selection of following questions. Experiments with simulated users show improvement over three alternative methods. Experiments in synthetic domains illuminate the domain characteristics under which the method is expected to be effective.

Vahid Jalali, David Leake

Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems

The quality of the cases maintained in a case base has a direct influence on the quality of the proposed solutions. The presence of cases that do not conform to the similarity hypothesis decreases the alignment of the case base and often degrades the performance of a CBR system. It is therefore important to find out the suitability of each case for the application of CBR and associate a solution with a certain degree of confidence. Feature weighting is another important aspect that determines the success of a system, as the presence of irrelevant and redundant attributes also results in incorrect solutions. We explore these problems in conjunction with a real-world CBR application called InfoChrom. It is used to predict the values of several soil nutrients based on features extracted from a chromatogram image of a soil sample. We propose novel feature weighting techniques based on alignment, as well as a new alignment and confidence measure as potential solutions. The hypotheses are evaluated on UCI datasets and the case base of Infochrom and show promising results.

Debarun Kar, Sutanu Chakraborti, Balaraman Ravindran

A Case-Based Approach to Mutual Adaptation of Taxonomic Ontologies

We present a general framework for addressing the problem of semantic intelligibility among artificial agents based on concepts integral to the case-based reasoning research program. For this purpose, we define case-based semiotics (

CBS

) (based on the well known notion of the semiotic triangle) as the model that defines semantic intelligibility. We show how traditional CBR notions like transformational adaptation can be used in the problem of two agents achieving mutual intelligibility over a collection of concepts (defined in

CBS

).

Sergio Manzano, Santiago Ontañón, Enric Plaza

A Lazy Learning Approach to Explaining Case-Based Reasoning Solutions

We present an approach to explanation in case-based reasoning (CBR) based on demand-driven (or lazy) discovery of explanation rules for CBR solutions. The explanation rules discovered in our approach resemble the classification rules traditionally targeted by rule learning algorithms, and the learning process is adapted from one such algorithm (PRISM). The explanation rule learned for a CBR solution is required to cover both the target problem and the most similar case, and is used together with the most similar case to explain the solution, thus integrating two approaches to explanation traditionally associated with different reasoning modalities. We also show how the approach can be generalized to enable the discovery of explanation rules for CBR solutions based on

k

-NN. Evaluation of the approach on a variety of classification tasks demonstrates its ability to provide easily understandable explanations by exploiting the generalizing power of rule learning, while maintaining the benefits of CBR as the problem-solving method.

David McSherry

Confidence in Workflow Adaptation

This paper is on assessing the quality of adaptation results by a novel confidence measure. The confidence is computed by finding evidence for partial solutions from introspection of a huge case base. We assume that an adaptation result can be decomposed into portions, that the provenance information for the portions is available. The adaptation result is reduced to such portions of the solution that have been affected by the change. Furthermore, we assume that a similarity measure for retrieving the portions from a case base can be specified and that a huge case base is available providing a solution space. The occurrence of each portion of the reduced solution in the case base is investigated during an additional retrieval phase after having adapted the case. Based on this idea of retrieving portions, we introduce a general confidence measure for adaptation results. It is implemented in the area of workflow adaptation. A graph-based representation of cases is used. The adapted workflow is reduced to a set of sub-graphs affected by the change. Similarity measures are specified for a graph matching method that implements the introspection of the case base. Experimental results on workflow adaptations from the cooking domain show the feasibility of the approach. The values of the confidence measure have been evaluated for three case bases with a size of 200, 2,000, and 20,000 cases each by comparing them with an expert assessment.

Mirjam Minor, Mohd. Siblee Islam, Pol Schumacher

Retrieval and Clustering for Business Process Monitoring: Results and Improvements

Business process monitoring is a set of activities for organizing process instance logs and for highlighting non-compliances and adaptations with respect to the default process schema. Such activities typically serve as the starting point for a-posteriori log analyses.

In recent years, we have implemented a tool for supporting business process monitoring, which allows to retrieve traces of process execution similar to the current one. Moreover, it supports an automatic organization of the trace database content through the application of clustering techniques. Retrieval and clustering rely on a distance definition able to take into account temporal information in traces.

In this paper, we report on such a tool, and present the newest experimental results.

Moreover, we introduce our recent research directions, that aim at improving the tool performances, usability and visibility with respect to the scientific community.

Specifically, we propose a methodology for avoiding exhaustive search in the trace database, by identifying promising regions of the search space, in order to reduce computation time.

Moreover, we describe how our work is being incorporated as a plug-in in ProM, an open source framework for process mining and process analysis.

Stefania Montani, Giorgio Leonardi

A Case-Based Approach to Cross Domain Sentiment Classification

This paper considers the task of sentiment classification of subjective text across many domains, in particular on scenarios where no in-domain data is available. Motivated by the more general applicability of such methods, we propose an extensible approach to sentiment classification that leverages sentiment lexicons and out-of-domain data to build a case-based system where solutions to past cases are reused to predict the sentiment of new documents from an unknown domain. In our approach the case representation uses a set of features based on document statistics, while the case solution stores sentiment lexicons employed on past predictions allowing for later retrieval and reuse on similar documents. The case-based nature of our approach also allows for future improvements since new lexicons and classification methods can be added to the case base as they become available. On a cross domain experiment our method has shown robust results when compared to a baseline single-lexicon classifier where the lexicon has to be pre-selected for the domain in question.

Bruno Ohana, Sarah Jane Delany, Brendan Tierney

GENA: A Case-Based Approach to the Generation of Audio-Visual Narratives

This paper presents GENA, a case-based reasoning system capable of generating audio-visual narratives by drawing from previously annotated content. Broadcast networks spend a large amount of resources in covering many events and many different types of audiences. However, it is not reasonable for them to cover smaller events or audiences, for which the cost would be greater than the potential benefits. For that reason, it is interesting to design systems that could automatically generate summaries, or personalized news shows for these smaller events or audiences. GENA was designed in collaboration with

Televisió de Catalunya

(the public Catalan broadcaster) precisely to address this problem. This paper describes GENA, and the techniques that were designed to address the complexities of the problem of generating audio-visual narrative. We also present an experimental evaluation in the domain of sports.

Santiago Ontañón, Josep Lluís Arcos, Josep Puyol-Gruart, Eusebio Carasusán, Daniel Giribet, David de la Cruz, Ismel Brito, Carlos Lopez del Toro

On Knowledge Transfer in Case-Based Inference

While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems:

knowledge transfer

. The issue we intend to elucidate can be expressed as follows:

what knowledge present in a source case is transferred to a target problem in case-based inference?

This paper presents a preliminary formal model of knowledge transfer and relates it to the classical notion of analogy.

Santiago Ontañón, Enric Plaza

Case-Based Aggregation of Preferences for Group Recommenders

We extend a group recommender system with a case base of previous group recommendation events. We show that this offers a new way of aggregating the predicted ratings of the group members. Using user-user similarity, we align individuals from the active group with individuals from the groups in the cases. Then, using item-item similarity, we transfer the preferences of the groups in the cases over to the group that is seeking a recommendation. The advantage of a case-based approach to preference aggregation is that it does not require us to commit to a model of social behaviour, expressed in a set of formulae, that may not be valid across all groups. Rather, the CBR system’s aggregation of the predicted ratings will be a lazy and local generalization of the behaviours captured by the neighbouring cases in the case base.

Lara Quijano-Sánchez, Derek Bridge, Belén Díaz-Agudo, Juan A. Recio-García

A Case-Based Solution to the Cold-Start Problem in Group Recommenders

We extend a group recommender system with a case base of previous group recommendation events. We show that this offers a potential solution to the cold-start problem. Suppose a group recommendation is sought but one of the group members is a new user who has few item ratings. We can copy ratings into this user’s profile from the profile of the most similar user in the most similar group from the case base. In other words, we copy ratings from a user who played a similar role in some previous group event. We show that copying in this way, i.e. conditioned on groups, is superior to copying nothing and also superior to copying ratings from the most similar user known to the system.

Lara Quijano-Sánchez, Derek Bridge, Belén Díaz-Agudo, Juan A. Recio-García

Opponent Type Adaptation for Case-Based Strategies in Adversarial Games

We describe an approach for producing exploitive and adaptive

case-based strategies

in adversarial games. We describe how

adaptation

can be applied to a precomputed, static

case-based strategy

in order to allow the strategy to rapidly respond to changes in an opponent’s playing style. The exploitive strategies produced by this approach tend to

hover

around a precomputed solid strategy and adaptation is applied directly to the precomputed strategy once enough information has been gathered to classify the current

opponent type

. The use of a precomputed,

seed

strategy avoids performance degradation that can take place when little is known about an opponent. This allows our approach an advantage over other exploitive strategies whose playing decisions rely on large, individual opponent models constructed from scratch. We evaluate the approach within the experimental domain of two-player Limit Texas Hold’em poker.

Jonathan Rubin, Ian Watson

Exploiting Extended Search Sessions for Recommending Search Experiences in the Social Web

HeyStaks is a case-based social search system that allows users to create and share case bases of search experiences (called

staks

) and uses these staks as the basis for result recommendations at search time. These recommendations are added to conventional results from Google and Bing so that searchers can benefit from more focused results from people they trust on topics that matter to them. An important point of friction in HeyStaks is the need for searchers to select their search context (that is, their active stak) at search time. In this paper we extend previous work that attempts to eliminate this friction by automatically recommending an active stak based on the searchers context (query terms, Google results, etc.) and demonstrate significant improvements in stak recommendation accuracy.

Zurina Saaya, Markus Schaal, Maurice Coyle, Peter Briggs, Barry Smyth

Event Extraction for Reasoning with Text

Textual Case-Based Reasoning (TCBR) aims at effective reuse of past problem-solving experiences that are predominantly captured in unstructured form. The absence of structure and a well-defined feature space makes comparison of these experiential cases difficult. Since reasoning is primarily dependent on retrieval of similar cases, the acquisition of a suitable indexing vocabulary is crucial for case representation. The challenge is to ensure that this vocabulary is selective and is representative enough to be able to capture the intended meaning in text, beyond simply the surface meaning. Indexing strategies that rely on bag of words (BOW) have the advantage of low knowledge acquisition costs, but only facilitate case comparison at a superficial level. In this paper we study the influence of semantic and lexical indexing constructs on a retrieve-only TCBR system applied to incident reporting. We introduce,

Rubee

(RUle-Based Event Extraction), an unsupervised approach for automatically extracting events from incident reports. A novel aspect of

Rubee

is its use of polarity information to distinguish between events that occurred and any non-event occurrences. Our results show that whilst semantic indexing is important, there is evidence that case representation benefits from a combined vocabulary (both semantic and lexical). A comparative study involving a popular event extraction system,

Evita

, and several baseline algorithms also indicate that events extracted by

Rubee

lead to significantly better retrieval performance.

Sadiq Sani, Nirmalie Wiratunga, Stewart Massie, Robert Lothian

Explanation-Aware Design of Mobile myCBR-Based Applications

This paper focuses on extending the explanation capabilities of the

myCBR

SDK as well as on the optimisation of the

myCBR

SDK in the context of Android-based mobile application development. The paper examines the available knowledge for explanation generation within context-aware CBR systems. The need for the integration of new explanation capabilities is then demonstrated by an Android-based context- and explanation-aware recommender application. Upon the experience gathered during implementation of the prototype a process for the integration of explanation capabilities into the

myCBR

SDK is introduced. Additionally, constraints and requirements for the integration of explanation capabilities into

myCBR

are introduced. Within this process we distinguish domain dependent and domain independent knowledge. We do this with regard to the different requirements for the integration of explanation capabilities into

myCBR

for the two types of knowledge. The paper further details on our on-going effort to adapt the

myCBR

SDK for use on the Android platform.

Christian Severin Sauer, Alexander Hundt, Thomas Roth-Berghofer

A Competitive Measure to Assess the Similarity between Two Time Series

Time series are ubiquitous, and a measure to assess their similarity is a core part of many systems, including case-based reasoning systems. Although several proposals have been made, still the more robust and reliable time series similarity measures are the classical ones, introduced long time ago. In this paper we propose a new approach to time series similarity based on the costs of iteratively jumping (or moving) between the sample values of two time series. We show that this approach can be very competitive when compared against the aforementioned classical measures. In fact, extensive experiments show that it can be statistically significantly superior for a number of data sources. Since the approach is also computationally simple, we foresee its application as an alternative off-the-shelf tool to be used in many case-based reasoning systems dealing with time series.

Joan Serrà, Josep Lluís Arcos

Case-Based Reasoning Applied to Textile Industry Processes

This paper describes an innovative usage of Case-Based Reasoning to reduce the high cost derived from correctly setting the textile machinery within the framework of European MODSIMTex project.

Furthermore, this system is capable of dealing with flexible queries, allowing to relax or restrict the searches in the case base. The paper discusses the ideas, design, implementation and an experimental evaluation of the CBR applied to the spinning scenario included in the project framework.

Beatriz Sevilla Villanueva, Miquel Sànchez-Marrè

Natural Language Generation through Case-Based Text Modification

Natural Language Generation (NLG) is one of the longstanding problems in Artificial Intelligence. In this paper, we focus on a subproblem in NLG, namely

surface realization through text modification

: given a source sentence and a desired change, produce a grammatically correct and semantically coherent sentence that implements the desired change. Text modification has many applications within text generation like interactive narrative systems, where stories tailored to specific users are generated by adapting or instantiating a pre-authored story. We present a case-based approach where cases correspond to pairs of sentences implementing specific modifications. We describe our retrieval, adaptation and revise procedures. The main contribution of this paper is an approach to perform case-adaptation in textual domains.

Josep Valls, Santiago Ontañón

Case-Based Reasoning for Turbine Trip Diagnostics

General Electric created a case-based reasoning system to diagnose unexpected shutdowns of turbines. This system is currently in use at a remote monitoring facility which monitors over 1500 turbines. There are multiple root causes for turbine shutdowns. The system has a reasoner for five of the most common root causes. These reasoners use either rule-based or case-based reasoning to produce a confidence value which specifies the likelihood that the root cause for that reasoner was responsible for the shutdown. Another case-based reasoner combines these individual confidences to determine the most likely root cause and its confidence.

Aisha Yousuf, William Cheetham

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