Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks

In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X × A → P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general, subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is desirable.

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USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS

Journal of Interconnection Networks ◽

10.1142/s0219265909002625 ◽

2009 ◽

Vol 10 (04) ◽

pp. 365-390

Author(s):

ALEJANDRO JUAN ◽

RICHARD W. PAZZI ◽

AZZEDINE BOUKERCHE

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Training Session ◽

Learning Classifier Systems ◽

Machine Learning Techniques ◽

Knowledge Map ◽

Classifier Systems ◽

Learning Classifier System ◽

Learning Classifier ◽

Virtual Simulations

Historically, the artificial intelligence (AI) of interactive virtual simulations or games is usually driven by pre-defined static scripts. One of the disadvantages of such scripted opponents is that they can be deciphered and countered by an intelligent user. Thus, the user has the opportunity to find weaknesses and an easy solution against the virtual simulation, which diminishes the efficiency aspect of a training session or entertaining value drastically. While randomization can be used to mask the static behaviour of a scripted AI, it is possible to develop much richer solutions by applying Learning Classifier System (LCS) techniques to create agents with intelligent-like behaviors. Learning Classifier Systems are rule-based machine learning techniques that rely on a Genetic Algorithm to discover a knowledge map used to classify an input space into a set of actions. In this paper, we propose the use of an unsupervised machine learning technique called Accuracy-based Learning Classifier Systems (XCS) for adaptable strategy generation that can be used in virtual simulations or games. XCS use a Genetic Algorithm to evolve a knowledge base in the form of rules. The performance and adaptability of the strategies and tactics developed with the XCS is analyzed by facing these against scripted opponents on a real time strategy game. According to our experiments, the rulesets are able to adapt to a wide array of behaviors from its opponents, as opposed to a linear game script, which is limited in its ability to adapt to its environment.

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SYMBIOTIC EVOLUTION OF RULE BASED CLASSIFIER SYSTEMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213009000019 ◽

2009 ◽

Vol 18 (01) ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

RAMIN HALAVATI ◽

SAEED BAGHERI SHOURAKI ◽

SIMA LOTFI ◽

POOYA ESFANDIAR

Keyword(s):

Genetic Algorithm ◽

Rule Base ◽

Classifier Systems ◽

Classification Rate ◽

Rule Based ◽

Sexual Recombination ◽

Number Of Genes ◽

Rule Sets ◽

Rule Based Classifier ◽

Rule Bases

Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a recombination operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design recombination operator, this paper presents a novel algorithm for rule base evolution based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. This operator takes two chromosomes with different number of genes (rules here) and merges them by combining all the information content of both chromosomes. Using this operator results in two major advantages: First, it totally removes the need to design the recombination operator and therefore is easier to use; second, it outperforms traditional genetic algorithm both in emergence speed and classification rate, this is tested and presented on some globally used benchmarks.

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Memory Exploitation in Learning Classifier Systems

Evolutionary Computation ◽

10.1162/evco.1994.2.3.199 ◽

1994 ◽

Vol 2 (3) ◽

pp. 199-220 ◽

Cited By ~ 21

Author(s):

Robert E. Smith

Keyword(s):

Genetic Algorithm ◽

Experimental Results ◽

Learning Classifier Systems ◽

Preliminary Evaluation ◽

Classifier Systems ◽

Performance Goal ◽

Computing Systems ◽

Future Directions ◽

Internal Memory ◽

Learning Classifier

Learning classifier systms (LCSs) offer a unique opportunity to study the adaptive exploitation of memory. Because memory is manipulated in the form of simple internal messages in the LCS, one can easily and carefully examine the development of a system of internal memory symbols. This study examines the LCS applied to a problem whose only performance goal is the effective exploitation of memory. Experimental results show that the genetic algorithm forms a relatively effective set of internal memory symbols, but that this effectiveness is directly limited by the emergence of parasite rules. The results indicate that the emergence of parasites may be an inevitable consequence in a system that must evolve its own set of internal memory symbols. The paper's primary conclusion is that the emergence of parasites is a fundamental obstacle in such problems. To overcome this obstacle, it is suggested that the LCS must form larger, multirule structures. In such structures, parasites can be more accurately evaluated and thus eliminated. This effect is demonstrated through a preliminary evaluation of a classifier corporation scheme. Final comments present future directions for research on memory exploitation in the LCS and similar evolutionary computing systems.

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