Transactions on Computational Collective Intelligence XXIX

This paper presents a fuzzy control and a conventional proportional derivative control for the electrode positioning system of a three-phase electric arc furnace. Generally, it is necessary to maintain constant the arc lengths for these kinds of furnaces. The two control strategies proposed in this paper regulates the current of the electric arc because arc length depends by the electric arc current. In order to do this, a new model of the electric arc developed by the authors of this paper was used. This paper illustrates a comparison of the performance analysis of a conventional PD controller and a fuzzy based intelligent controller. The fuzzy intelligent based controller has two inputs and one output. The whole systems are simulated by using of Matlab/Simulink software. These systems are tested when applying a step disturbance in the process, when applying a sequence of step disturbances and when applying a second step disturbance before being compensate the effect of another disturbance. The responses of the closed-loop systems illustrates that the proposed fuzzy controller has better dynamic performance, rapidity and good robustness as compared to the proposed PD controller.

In this paper fuzzy type-1 and type-2 models for control of bionic hand in real-time are proposed. The control process involves interpretation and analysis of surface electromyography signal (sEMG) acquired from patients with amputees. In the provided experiments, we assume the use of force sensing resistor to achieve better control of the bionic hand being modeled. The classical type-1 Mamdani control as well as the extended type-2 fuzzy models are considered for this application. The conducted experiments show comparable results with respect to applied assumptions that give the confidence to implement the proposed concept into real-time control process.

The article deals with the problem of selecting an advertisement variant on the basis of dynamically-changing values of evaluation criteria. Therefore, a framework, used in an online environment, of a dynamic multi-criteria decision analysis (DMCDA) has been prepared. The framework was based on the PROMETHEE method which makes it possible to carry out a very detailed analysis of a decision process and obtained solutions. While applying the prepared framework, a number of ad variants were considered on the basis of the data collected during a subjective study and a field experiment. In the course of solving the decision problem, the advertiser’s and website operator’s perspectives as well as two aggregation strategies of dynamic data were considered. As a result, the following was obtained: partial rankings of variants, global rankings considering the advertiser’s and publisher’s points of view, GDSS rankings pointing to compromise solutions by merging the two points of view. The obtained solutions were verified by means of: examining correlation coefficients, a GAIA analysis and an analysis of ranking robustness to preference changes. The end result was that the most satisfying advertiser and publisher were determined.

The aim of this paper is to suggest a framework for adaptive agent decision-making modeling of biological regulation and psychological mechanisms. For this purpose, first, a perception-action cycle scheme for the agent-environment interactions and the deduced framework for adaptive agent decision-making modeling are developed. Second, motivation systems: drives (homeostatic regulation), personality traits (five-factor model), and emotions (basic emotions) are developed. Third, a neural architecture implementation of the framework is suggested. Then, first tests related to a stimulation-drive (from a moving object), for two different agent personalities, and the activation level of emotions are presented and analyzed. Finally, a discussion is given in order to highlight important problems related to the adaptive decision-making behavior, the common currency that should have each system in the suggested framework, and the neural architecture, as well as to detail the ways they are solved. The obtained results demonstrate how the personality and emotion of the agent can be used to regulate the intensity of the interaction; predicting a promising result in future: to demonstrate how the nature of the interaction (stimulation-drive, social-drive, …) influences the agent behavior which could be very interesting for cooperative agents.

The emergency management offers a collection of methods, strategies and frameworks how to efficiently solve particular emergency situation for saving human lives with minimum spending time. Fast and faultless decision making is inevitable in these situations. This requires huge amount of theoretical and practical experience which can be gained during studying of particular courses, study programmes, or where we are directly confronted with a reality. Non-professionals have a problem to decide about the best sequence of steps which has to be applied if a particular emergency event occurs. The main aim of this text is to present the SWRL-based prototype for decision making if the first aid is necessary.

The paper discusses two proposed methods for the cost optimization of the deterministic queuing systems based on the control of the queue lengths. The first method uses the evaluation of actual states at the particular service places according to their development. The decision is then based on the comparison of the criteria of productivity and the expended costs. The suggested change in the system setting with the highest priority is then accomplished. The second method is based on the simulation of the future states and on this basis the appropriate time and type of the modification of the system setup is suggested.

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retrieval, machine translation, information extraction because of its semantics understanding. This paper describes the proposed approach W3SD (This paper is an extended version of our work [4] published in the 8^th International Conference on Computational Collective Intelligence.) which is based on the words surrounding the polysemous word in a context. Each meaning of these words is represented by a vector composed of weighted nouns using WordNet and Wiktionary features through the taxonomic information content from WordNet and the glosses from Wiktionary. The main emphasis of this paper is feature selection for disambiguation purpose. The assessment of WSD systems is discussed in the context of the Senseval campaign, aiming at the objective evaluation of our proposal to the systems participating in several different disambiguation tasks.

Constrained scheduling problems are quite common in project management, manufacturing, distribution, transportation, logistics, supply chain management, software engineering, and computer networks etc. The need to use integer and binary decision variables representing the allocation of different resources to many activities and numerous specific, universal and additional constraints on these decision variables are typical components of the resource-constrained scheduling problems (RCSPs) modeling. It is often necessary to model additional resources and constraints. For these reasons, models are becoming computationally demanding. This is particularly noticeable when methods of operations research (mathematical programing (MP), network programing, and dynamic programming) are used. On the other hand, most RCPSs can be easily modeled as instances of the constraint satisfaction problems (CSPs) and solved using constraint programming (CP) methods. In contrast to the MP-based environment, the CP-based environment methods deal well with binary constraints but worse in optimization. Therefore, the hybrid approach based on integration mathematical programming and constraint logic programming to optimization resource-constrained scheduling problems has been proposed. To evaluate the applicability and computational efficiency of the proposed approach and its implementation programming framework, the illustrative examples of optimization resource-constrained scheduling problems are implemented separately for mathematical programming and hybrid method.

In this paper, we analyze the opportunities of the structured information of the social networks for the subjectivity detection on Twitter micro texts. The sentiment analysis on Twitter has been usually performed through the automatic processing of the texts. However, the established limit of 140 characters and the particular characteristics of the texts reduce drastically the accuracy of Natural Language Processing (NLP) techniques when compared with other domains. Under these circumstances, it becomes necessary to study new data sources that allow us to extract new useful knowledge to represent and classify the texts. The structured information, also called meta-information or meta-data, provide us with alternative features of the texts that can improve the classification tasks. In this paper we analyze the features of the structured information and their usefulness in the opinion mining sub-domain, specially in the subjectivity detection task. Also present a novel classification of these features according to their origin.

This paper presents our approach to optimize the performance of both reading and writing concurrent operations on large-scale social networks. Here, we focus on the directed and unweighted relationships among members in a social network. It can then be illustrated as a directed, unweighted graph. Moreover, determining the relationship between any two members is similar to finding the shortest path between two vertices. With such a large-scale dynamic social network, we face the problem of having concurrent operations from adding or removing edges dynamically while one may ask to determine the relationship between two members. To solve this issue, we propose an efficient parallel method based on (i) utilizing an appropriate data structure, (ii) parallelizing the updating actions and (iii) improving the performance of query processing by both reducing the searching space and computing in multi-threaded parallel. Our method was validated by the datasets from SigMod Contest 2016 and SNAP DataSet Collections with fine positive experimental results compared to other solutions.