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  5. A nondominated adversarial search algorithm for a threeplayer chess game
 
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A nondominated adversarial search algorithm for a threeplayer chess game

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Author(s)
Karnjanapoomi, N.
Pramanpol, P.
Lertratsamewong, V.
Chacavarnkitkuln, T.
Rattanajongjittakorn, V.
Thavaravej, C.
Wattanapornprom, W.
Rodkaew, Y.
Other Contributor(s)
University of the Thai Chamber of Commerce. Research Support Office
Publisher(s)
University of the Thai Chamber of Commerce
Date Issued
2013
Resource Type
Text::Conference output::Conference proceedings::Conference paper
Language
English
Abstract
Threeplayer (3P) chess is a variation of chess game specially designed for three players. The winning condition that the first player will wins if the player captures any of the other's King, arises the playing strategies that a player can form a temporarily alliance with another player or a player can take advantage from the other's attacking to defeat either two of the opponents. This paper proposes a search algorithm called the nondominated adversarial search (NAS) for the threeplayer chess game which does not only maximize the own score but also minimize the opponents' scores. The experiment shows that the NAS algorithm can take advantage from the other's attacking to win the game.
Subject(s)
Computer and Multimedia Engineering
Keywords(s)
  • artificial intelligen...

  • game

  • Minimax algorithm

  • Nondominated sorting

  • three player chess

Conference
2013 International Computer Science and Engineering Conference
Access Rights
public
Rights
This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Rights Holder
University of the Thai Chamber of Commerce
Bibliographic Citation
N. Karnjanapoomi, P. Pramanpol, V. Lertratsamewong, T. Chacavarnkitkuln, V. Rattanajongjittakorn, C. Thavaravej, W. Wattanapornprom, Y. Rodkaew (2013) A nondominated adversarial search algorithm for a threeplayer chess game., 196-200.
URI
https://hdl.handle.net/20.500.14437/3461
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Acquisition Date
Jun 4, 2026
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