GA-based Action Learning

Satoshi Yonemoto

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Paper

GA-based Action Learning

Topics: Genetic Algorithms

In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, 293-298, 2015 , Lisbon, Portugal

Author: Satoshi Yonemoto

Affiliation: Kyushu Sangyo University, Japan

Keyword(s): Genetic Algorithm, Action Learning and Action Series Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: This paper describes a GA-based action learning framework. First, we propose a GA-based method for action learning. In this work, GA is used to learn perception-action rules that cannot be represented as genes directly. The chromosome with the best fitness (elitist) acquires the perception-action rules through the learning process. And then, we extend the method to action series learning. In the extended method, action series can be treated as one of perception-action rules. We present the experimental results of three controllers (simple game AI testbed) using the GA-based action learning framework.

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Paper citation in several formats:

Yonemoto, S. (2015). GA-based Action Learning. In Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - ECTA; ISBN 978-989-758-157-1, SciTePress, pages 293-298. DOI: 10.5220/0005613902930298

@conference{ecta15,
author={Satoshi Yonemoto.},
title={GA-based Action Learning},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - ECTA},
year={2015},
pages={293-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005613902930298},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - ECTA
TI - GA-based Action Learning
SN - 978-989-758-157-1
AU - Yonemoto, S.
PY - 2015
SP - 293
EP - 298
DO - 10.5220/0005613902930298
PB - SciTePress