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Computational intelligence techniques involve the use of computers to enable machines to simulate human performance. The prominent paradigms used include AI systems, artificial neural networks, multimedia, fuzzy logic, evolutionary computing techniques, artificial life, computer vision, adaptive intelligence, and chaos engineering. These knowledge-based computational intelligence techniques have generated...
We have been exploring the potential for a co-evolutionary process to learn how to play checkers without relying on the usual inclusion of human expertise in the form of features that are believed to be important to playing well. In particular, we have focused on the use of a population of neural networks, where each network serves as an evaluation function to describe the quality of the current board...
The main objective of this chapter is to present a comparative study of two techniques that automatically generate useful knowledge in games. Retrograde analysis of patterns generates pattern databases, starting with a simple definition of a sub-goal in a game and progressively finding all the pattern of given sizes that fulfill this sub-goal. Metaprogramming is based on similar concepts, but instead...
The game of Go has a high branching factor that defeats the tree search approach used in computer chess, and long-range spatiotemporal interactions that make position evaluation extremely difficult. Development of conventional Go programs is hampered by their knowledge-intensive nature. We demonstrate a viable alternative by training neural networks to evaluate Go positions via temporal difference...
We use reinforcement learning (RL) to evolve soccer team strategies. RL may profit significantly from world models (WMs). In high-dimensional, continuous input spaces, however, learning accurate WMs is intractable. In this chapter, we show that incomplete WMs can help to quickly find good policies. Our approach is based on a novel combination of CMACs and prioritized sweeping. Variants thereof outperform...
In this chapter, we describe how fuzzy rule-based systems can be applied to a market selection game with many players (e.g., 100 players) and several markets (e.g., five markets). Our market selection game is a non-cooperative repeated game where every player is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It...
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