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This focus area applies various computational intelligence approaches to the wide domain of games. Systems are developed for the effective playing of a variety of competitive games against humans and other computer-based systems.

Currently, the specific research focus is on the use of coevolutionary strategies to train game playing agents, as well as novel approaches for analysing the behaviour of these agents.


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Coevolving Probabilistic Game Playing Agents using Particle Swarm Optimization Algorithms
Papacostantis, E. Engelbrecht, AP. Franken, N. 2005.
IEEE Symposium on Computational Intelligence and Games, Colchester, Essex, UK, 2005, 195-202, IEEE

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Coevolutionary techniques in combination with particle swarm optimization algorithms and neural networks have shown to be very successful in finding strong game playing agents for a number of deterministic games. This paper investigates the applicability of a PSO coevolutionaryapproach to probabilistic games. For the purposes of this paper, a probabilistic variation of the tic-tac-toe game is used. Initially, the technique is applied to a simple deterministic game (tic-tac-toe), proving its effectiveness with such games. The technique is then applied to a probabilistic 4x4x4 tic-tac-toe game, illustrating scalability to more complex, probabilistic games. The performance of the probabilistic game agent is compared against agents that move randomly. To determine how these game agents compete against strong non-random game playing agents, coevolved solutions are also compared against agents that utilize a strong hand-crafted static evaluation function. Particle swarm optimization parameters/topologies and neural network architectures are experimentally optimized for the probabilistic tic-tac-toe game.

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Composing linear evaluation functions from observable features
Duminy, WH. Engelbrecht, AP. 2005.
SACJ, No. 35., 2005, 48-58, South African Computing Journal

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Discovering useful knowledge is a problem that remains elusive, even for publicly solved games such as Checkers. This paper presents a formal language F that represents knowledge that can be used by two-player board game agents. The language assumes minimal domain knowledge and as basic symbols it employs information that can be obtained directly from the observation of a game position. The language operators can be used to construct and manipulate knowledge within the context of an evaluation function.

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