Evolutionary multiobjective optimization for memory-encoding controllers in the artificial ant problem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Many agent problems need efficient controllers that the agent takes to handle the environmental information. If the sensor information about the environment is limited, dynamic processing of internal memory is required. An agent solves the artificial ant problem with internal memory, where an agent is supposed to collect all the food pellets on the trails. In this paper, we provide an evolutionary multiobjective optimization approach to quantify the amount of memory needed for desirable behavior performance for the agent problem. For the approach, we use finite state controllers to encode internal memory. The approach uses two objectives, number of internal states and behavior performance. The goal is to maximize the behavior performance of the agent with each level of internal states. The suggested method with elitism strategy can find efficiently desirable controllers for the artificial and problem.

Original languageEnglish
Title of host publicationIEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011
Subtitle of host publication2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making
Pages73-80
Number of pages8
DOIs
Publication statusPublished - 2011 Aug 10
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, MCDM 2011 - Paris, France
Duration: 2011 Apr 112011 Apr 15

Publication series

NameIEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making

Other

OtherSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, MCDM 2011
CountryFrance
CityParis
Period11/4/1111/4/15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Decision Sciences (miscellaneous)

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    Kim, D. (2011). Evolutionary multiobjective optimization for memory-encoding controllers in the artificial ant problem. In IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (pp. 73-80). [5949287] (IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making). https://doi.org/10.1109/SMDCM.2011.5949287