Abstract. In this article we investigate the feasibility of using learning classifier systems as a tool for building adaptive control systems for real robots. Their use on real robots imposes efficiency eonstraints which are addressed by three main tools: parallelism, distributed architecture, and training. Parallelismis useful to speed up computation and to increase the flexibility of the learning system design. Distributed architecture helps in making it possible to deeompose the overall task into a set of simpler learning tasks. Finally, training provides guidance to the system while learning, shortening the number of cycles required to learn. These tools and the issues they raise are first studied in simulation, and theu the experience gained with simulations is used to implement the learning system on the real robot. Results have shown that with this approach it is possible to let the AutonoMouse, a small real robot, learn to approach a light source under a number of different noise and lesion conditions. Keywords: learning classifier systems, reinforcement learning, genetic algorithms, animat problem 1.

This work investigates some uses of self-monitoring in classifier systems (CS) using Wilson's recent XCS system as a framework. XCS is a significant advance in classifier systems technology which shifts the basis of fitness evaluation for the Genetic Algorithm (GA) from the strength of payoff prediction to the accuracy of payoff prediction. Initial work consisted of implementing an XCS system in Pop11 and replicating published XCS multiplexer experiments from (Wilson 1995, 1996a). In subsequent original work, the XCS Optimality Hypothesis, which suggests that under certain conditions XCS systems can reliably evolve optimal populations (solutions), is proposed. An optimal population is one which accurately maps inputs to actions to reward predictions using the smallest possible set of classifiers. An optimal XCS population forms a complete mapping of the payoff environment in the reinforcement learning tradition, in contrast to traditional classifier systems which only seek to maximise ...

Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding

Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, using illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

Intelligent robots should be able to use sensor information to learn how to behave in a changing environment. As environmental complexity grows, the learning task becomes more and more difficult. We face this problem using an architecture based on learning classifier systems and on the structural properties of animal behavioural organization, as proposed by ethologists. After a description of the learning technique used and of the organizational structure proposed, we present experiments that show how behaviour acquisition can be achieved. Our simulated robot learns to follow a light and to avoid hot dangerous objects. While these two simple behavioural patterns are independently learnt, coordination is attained by means of a learning coordination mechanism. Again this capacity is demonstrated by performing a number of experiments. 2 I. Introduction The traditional knowledge-based approach to artificial intelligence shows some fundamental deficiencies in the generation of powerful ...

Abstract- Intelligent robots should be able to use sensor information to learn how to behave in a changing environment. As environmental complexity grows, the learning task becomes more and more difficult. This problem is faced using an architecture based on learning classifier systems and on the structural properties of animal behavioral organization, as proposed by ethologists. After a description of the learning technique used and of the organizational structure proposed, experiments that show how behavior acquisition can be achieved were presented. The simulated robot learns to follow a light and to avoid hot dangerous objects. While these two simple behavioral patterns are independently learned, coordination is attained by means of a learning coordination mechanism. Again this capacity is demonstrated by performing a number of experiments. I.

This document describes the CFS-C system, a package of subroutines (and data structures) that can be used to implement learning classifier systems for arbitrary, user-defined taskdomains /environments. The CFS-C subroutines implement the core, domain-independent parts of a classifier system, including routines to implement the following steps of the "major-cycle" of a classifier system:

This work describes a control architecture based on a hierarchical classifier system. This system, which learns both reactive and planning rules, implements a motivationally autonomous animat that chooses the actions it performs according to its perception of the external environment, to its physiological or internal state, to the consequences of its current behavior, and to the expected consequences of its future behavior. The adaptive faculties of this architecture are illustrated within the context of a navigation task, through various experiments with a simulated and a real robot. I. Introduction The work presented in this paper fits into the so-called animat approach, which aims at designing animats, i.e., simulated animals or real robots whose rules of behavior are inspired by those of animals. The proximate goal of this approach is to discover architectures or working principles that allow an animal or a robot to exhibit an adaptive behavior and, thus, to survive or fulfill i...

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This paper describes an extension of a GAbased, separate-and-conquer propositional rule induction algorithm called SIA [24]. While the original algorithm is computationally attractive and is also able to handle both nominal and continuous attributes efficiently, our algorithm further improves it by taking into account of the recent advances in the rule induction and evolutionary computation communities. The refined system has been compared to other GA-based and non GA-based rule learning algorithms on a number of benchmark datasets from the UCI machine learning repository. Results show that the proposed system can achieve higher performance while still produces a smaller number of rules. 1 Introduction The increasingly widespread use of information system technologies and the internet has resulted in an explosive growth of many business, government and scientific databases. As these terabyte-size databases become prevalent, the traditional approach of using human experts to sift thro...