Co-Evolutionary Particle Swarm Optimization Applied to the 7x7 Seega Game

• Presented at: International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July, 2004
• Published in: Proceedings of the International Joint Conference on Neural Networks (IJCNN)
• Publication date: 2004

Seega is an ancient Egyptian two-stage board game that, in certain aspects, is more difficult than chess. The two-player game is most commonly played on a 7 × 7 board, but is also sometimes played on a 5 × 5 or 9 × 9 board. In the first and more difficult stage of the game, players take turns placing one disk each on the board until the board contains only one empty cell. In the second stage players take turns moving disks of their color; a disk that becomes surrounded by disks of the opposite color is captured and removed from the board. Building on previous work, on the 5 × 5 version of Seega, we focus, in this paper, on the 7 × 7 board. Our approach employs co-evolutionary particle swarm optimization for the generation of feature evaluation scores. Two separate swarms are used to evolve White players and Black players, respectively; each particle represents feature weights for use in the position evaluation. Experimental results are presented and the performance of the full game engine are discussed.

Keywords: Particle Swarm Optimization ; Co-evolution ; Game ; Evolutionary Robotics

Reference

• LIS-CONF-2006-019

Record created on 2006-10-05, modified on 2017-05-10