Neural learning of heuristic functions for general game playing

Abstract

The proposed model represents an original approach to general game playing, and aims at creating a player able to develop a strategy using as few requirements as possible, in order to achieve the maximum generality. The main idea is to modify the known minimax search algorithm removing its task-specific component, namely the heuristic function: this is replaced by a neural network trained to evaluate the game states using results from previous simulated matches. A method for simulating matches and extracting training examples from them is also proposed, completing the automatic procedure for the setup and improvement of the model. Part of the algorithm for extracting training examples is the Backward Iterative Deepening Search, a new original search algorithm which aims at finding, in a limited time, a high number of leaves along with their common ancestors.