Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Orhand:2022:EuroGP,

• author = "Romain Orhand and Anne Jeannin-Girardon and Pierre Parrend and Pierre Collet",
• title = "Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems",
• booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming",
• year = "2022",
• editor = "Eric Medvet and Gisele Pappa and Bing Xue",
• series = "LNCS",
• volume = "13223",
• publisher = "Springer Verlag",
• address = "Madrid, Spain",
• pages = "245--261",
• month = "20-22 " # apr,
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Anticipatory Learning Classifier System, Machine learning, Explainability, Non-determinism, Building Knowledge: Poster",
• isbn13 = "978-3-031-02055-1",
• DOI = "doi:10.1007/978-3-031-02056-8_16",
• 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 introduces 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. The Probability-Enhanced Predictions consist in enabling the anticipation of several states, while the Behavioral Sequences permits the construction of sequences of actions. The capabilities of BEACS have been studied on a thorough benchmark of 23 mazes and the results show that BEACS can handle different kinds of non-determinism in partially observable environments, while describing completely and more accurately such environments. BEACS thus provides explanatory insights about created decision policies and environmental representations.",
• notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022",

}

Genetic Programming entries for Romain Orhand Anne Jeannin-Girardon Pierre Parrend Pierre Collet

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