Abstract-Game theory is a tool that may be used to model a player as an intelligent being -one who seeks to optimize his own performance while taking into account the performance of his opponent. However, it is often challenging to apply the theory in practice. In the naval environment, this approach may be used, for instance, to find the best strategy for an Autonomous Underwater Vehicle (AUV) while considering the intelligence of the submarine opponent. Classic approaches based on Minimax suffer from an explosion of states, and they are difficult to use in real-time. The paper introduces an approach that improves the Minimax algorithm in a complex naval environment. It assumes limited and scalable computational resources. The approach takes advantage of a flexible utility function based on a neural network with parameters tuned by a genetic algorithm.
Abstract-The paper presents a new method of building a hierarchical model of the state space. The model is extracted fully automatically from game replays that store executed plan traces. It is used by a novel approach for estimating the distance between states in a state-space graph. The estimate is applied in the A* algorithm as a heuristic function to reduce the search space. The method was validated using the game Smart Blocks. It is a testbed environment for studying methods that benefit from game replay analysis. The proposed heuristic is dedicated to difficult classical planning problems, for which problem-specific or automated heuristics are difficult to obtain.
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