Behavior learning of autonomous agents in continuous state using function approximation

Min Kyu Shon, Junichi Murata

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper presents a method for behavior learning of an autonomous agent using modified Learning Vector Quantization (LVQ) with fuzzy sets in continuous state space. When the environment is a continuous state space, it has infinitely many state values. So, it is impossible to learn a good action to take in each of the state values. This paper uses a function approximation technique based on the LVQ algorithm to learn actions of agent in continuous state space. An advantage of this technique is that it can represent the mapping between the continuous-valued state space and appropriate actions with a finite number of parameters. An example illustrates its validity in continuous space problems.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Verlag
Pages1213-1219
Number of pages7
ISBN (Print)9783540301325
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3213
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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