Case indexing using pso and ann in real time strategy games

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

doi:10.1007/978-3-642-11164-8_18

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 proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings
Pages	106-115
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-11164-8_18
Publication status	Published - 1 Dec 2009
Event	3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009 - New Delhi, India Duration: 16 Dec 2009 → 20 Dec 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5909 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009
Country/Territory	India
City	New Delhi
Period	16/12/09 → 20/12/09

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-642-11164-8_18

Cite this

Huo, P., Shiu, C. K. S., Wang, H., & Niu, B. (2009). Case indexing using pso and ann in real time strategy games. In Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings (pp. 106-115). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5909 LNCS). https://doi.org/10.1007/978-3-642-11164-8_18

Huo, Peng ; Shiu, Chi Keung Simon ; Wang, Haibo et al. / Case indexing using pso and ann in real time strategy games. Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings. 2009. pp. 106-115 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Case indexing using pso and ann in real time strategy games",

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.",

author = "Peng Huo and Shiu, {Chi Keung Simon} and Haibo Wang and Ben Niu",

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Huo, P, Shiu, CKS, Wang, H & Niu, B 2009, Case indexing using pso and ann in real time strategy games. in Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5909 LNCS, pp. 106-115, 3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009, New Delhi, India, 16/12/09. https://doi.org/10.1007/978-3-642-11164-8_18

Case indexing using pso and ann in real time strategy games. / Huo, Peng; Shiu, Chi Keung Simon; Wang, Haibo et al.
Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings. 2009. p. 106-115 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5909 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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Huo P, Shiu CKS, Wang H, Niu B. Case indexing using pso and ann in real time strategy games. In Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings. 2009. p. 106-115. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-11164-8_18

Case indexing using pso and ann in real time strategy games

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