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10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006 and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Papers
In this paper we promote a new methodology for designing LCS that is based on first identifying their underlying model and then using standard machine learning methods to train this model. This leads to a clear identification of the LCS model and makes explicit the assumptions made about the data, as well as promises advances in the theoretical understanding of LCS through transferring the understanding...
This paper provides a deep insight into the learning mechanisms of UCS, a learning classifier system (LCS) derived from XCS that works under a supervised learning scheme. A complete description of the system is given with the aim of being useful as an implementation guide. Besides, we review the fitness computation, based on the individual accuracy of each rule, and introduce a fitness sharing scheme...
Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain complicated and active plant behaviors. To uncover complex plant behaviors, this paper proposes a new method of developing a process response model from continuous time-series...
There are few contributions to robot autonomous navigation applying Learning Classifier Systems (LCS) to date. The primary objective of this work is to analyse the performance of the strength-based LCS and the accuracy-based LCS, named EXtended Learning Classifier System (XCS), when applied to two distinct robotic tasks. The first task is purely reactive, which means that the action to be performed...
Over the recent years, research on Learning Classifier Systems (LCSs) got more and more pronounced and diverse. There have been significant advances of the LCS field on various fronts including system understanding, representations, computational models, and successful applications. In comparison to other machine learning techniques, the advantages of LCSs have become more pronounced: (1) rule-comprehensibility...
This paper presents an approach to analyze population evolution in classifier systems using a symbolic representation. Given a sequence of populations, representing the evolution of a solution, the method simplifies the classifiers in the populations by reducing them to their “canonical form”. Then, it extracts all the subexpressions that appear in all the classifier conditions and, for each subexpression,...
Utilising the expressive power of S-Expressions in Learning Classifier Systems often prohibitively increases the search space due to increased flexibility of the encoding. This work shows that selection of appropriate S-Expression functions through domain knowledge improves scaling in problems, as expected. It is also known that simple alphabets perform well on relatively small sized problems in a...
This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. Fuzzy-UCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a large collection of real-world problems, and compared to UCS and three highly-used machine learning...
The estimation of the classifier error plays a key role in accuracy-based learning classifier systems. In this paper we study the current definition of the classifier error in XCSF and discuss the limitations of the algorithm that is currently used to compute the classifier error estimate from online experience. Subsequently, we introduce a new definition for the classifier error and apply the Bayes...
This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets and introduce a new technique for visualizing explanatory...
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of an accuracy-based scheme where fitness is based on a rule’s ability to predict the expected payoff from its use. Learning Classifier Systems that build anticipations of the expected...
This article describes a learning classifier system (LCS) approach to relational reinforcement learning (RRL). The system, Foxcs-2, is a derivative of Xcs that learns rules expressed as definite clauses over first-order logic. By adopting the LCS approach, Foxcs-2, unlike many RRL systems, is a general, model-free and “tabula rasa” system. The change in representation from bit-strings in Xcs to first-order...
This paper reviews a competent Pittsburgh LCS that automatically mines important substructures of the underlying problems and takes problems that were intractable with first-generation Pittsburgh LCS and renders them tractable. Specifically, we propose a χ-ary extended compact classifier system (χeCCS) which uses (1) a competent genetic algorithm (GA) in the form of χ-ary extended compact genetic...
The classifier system XCSF was modified to use gene expression programming for the evolution and functioning of the classifier conditions. The aim was to fit environmental regularities better than is typically possible with conventional rectilinear conditions. An initial experiment approximating a nonlinear oblique environment showed excellent fit to the regularities.
XCS with computed prediction, namely XCSF, extends XCS by replacing the classifier prediction with a parametrized prediction function. Although several types of prediction functions have been introduced, so far XCSF models are still limited to evolving classifiers with the same prediction function. In this paper, we introduce XCSF with heterogeneous predictors, XCSFHP, which allows the evolution of...
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model, which represents salient interactions between attributes for a given data, (2) a surrogate model, which provides a functional approximation of the output as a function of attributes,...
Ensemble techniques have proved to be very successful in boosting the performance of several types of machine learning methods. In this paper, we illustrate its usefulness in combination with GAssist, a Pittsburgh-style Learning Classifier System. Two types of ensembles are tested. First we evaluate an ensemble for consensus prediction. In this case several rule sets learnt using GAssist with different...
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