Case learning and indexing in real time strategy games

Haibo Wang, Peter H.F. Ng, Ben Niu, Chi Keung Simon Shiu

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

1 Citation (Scopus)

Abstract

Development of real time strategy game AI is a challenging and difficult task. However, the current architecture of game applications doesn 't support well the utilization of user contributed contents to get better game playability. The portability of the algorithms is quite poor due to the use of the problem specific heuristics. Real-time learning may be a possible solution, but it involves long training time. In this paper, we propose a case indexing method using neural-evolutionary learning approach in a "tower defense"-style real time strategy (RTS) game. Artificial Neural Network (ANN) is trained on the cannon placement combinations by the result of Genetic Algorithm (GA). This model provides an efficient indexing of past experience. Experimental results are provided to illustrate our idea.
Original languageEnglish
Title of host publication5th International Conference on Natural Computation, ICNC 2009
Pages100-104
Number of pages5
Volume1
DOIs
Publication statusPublished - 1 Dec 2009
Event5th International Conference on Natural Computation, ICNC 2009 - Tianjian, China
Duration: 14 Aug 200916 Aug 2009

Conference

Conference5th International Conference on Natural Computation, ICNC 2009
Country/TerritoryChina
CityTianjian
Period14/08/0916/08/09

Keywords

  • Artificial neural network
  • Case-based planning
  • Ggenetic algorithm
  • Real time strategy (RTS) games

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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