Romain Orhand

In Proceedings of the companion conference on genetic and evolutionary computation

Abstract Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a topic of interest to get more insights into the discovered underlying patterns from the data or to remove useless classifiers from the populations. However, these techniques have neither been used nor adapted to Anticipatory Learning Classifier Systems (ALCS). ALCS differ from other LCS in that they build models of their environments from which decision policies to solve their learning tasks are learned.

Accurate and interpretable representations of environments with anticipatory learning classifier systems

By Romain Orhand, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

2022-01-01

In European conference on genetic programming (part of EvoStar)

Abstract Anticipatory Learning Classifier Systems (ALCS) are rule- based machine learning algorithms that can simultaneously develop a complete representation of their environment and a decision policy based on this representation to solve their learning tasks. This paper intro- duces BEACS (Behavioral Enhanced Anticipatory Classifier System) in order to handle non-deterministic partially observable environments and to allow users to better understand the environmental representations issued by the system. BEACS is an ALCS that enhances and merges Probability-Enhanced Predictions and Behavioral Sequences approaches used in ALCS to handle such environments.

CRACS: Compaction of rules in anticipatory classifier systems

Accurate and interpretable representations of environments with anticipatory learning classifier systems

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