Case indexing using pso and ann in real time strategy games

Peng Huo, Chi Keung Simon Shiu, Haibo Wang, Ben Niu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

This paper proposes a case indexing method using particle swarm optimization (PSO) and artificial neural network (ANN) in a defense-style real time strategy (RTS) game. PSO is employed to optimize the placement of cannons to defend the enemy attack. The execution time of PSO (> 100 seconds) is unsatisfied for RTS game. The result of PSO is used as a case indexing of past experience to train ANN. After the training (approximately 30 seconds), ANN can obtain the best cannon placement within 0.05 second. Experimental results demonstrated that this case indexing method using PSO and ANN efficiently speeded up the whole process to satisfy the requirement in RTS game.
Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings
Pages106-115
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2009
Event3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009 - New Delhi, India
Duration: 16 Dec 200920 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5909 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009
Country/TerritoryIndia
CityNew Delhi
Period16/12/0920/12/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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