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This article presents the development and study of a model for formalizing the decision-making process in a computer game using artificial intelligence methods. Game artificial intelligence is implemented using a rather limited set of tools: artificial neural networks, evolutionary algorithms, specialized architectures for building artificial intelligence systems. But neural networks also have a number of disadvantages. Perhaps the most significant of them is the complexity and long network training time. The problem is raised of developing new mathematical algorithms that have a probability of making the right decision, comparable to neural networks, but with less training time. At the moment, there is no single model of the artificial immune system for the development of artificial intelligence in computer and mobile games. Thus, the problem is raised of developing new algorithms that allow realizing interaction with the player in a computer game, having speed and realism comparable to artificial neural networks and expert systems, and at the same time having less training time. One of the ways to solve this problem is to develop a game artificial intelligence model based on an artificial immune system. The dot game is a modification of the Japanese game go. In this game, positional strategic planning is combined with tactical enumeration of options. The implementation of artificial intelligence in this game in the form of a decision tree is not rational for solving this problem. To simplify the search for the optimal solution, heuristic rules were used. To reduce the solution search time, game artificial intelligence was created using methods based on the principles of the immune system. The artificial immune system is an idealized version of the natural analogue and reproduces the key components of the natural process: selection of the best antibodies in the population depending on the degree of their affinity (proximity) to the antigen, antibody cloning, antibody mutation. |
