Multi-Minimax: A New AI Paradigm for Simultaneously-Played Multi-player Games

Abstract

The best reported methods for turn-based multi-player game playing AI algorithms include Max$^n$, Paranoid and Best Reply Search. All of these methods make decisions by modelling the game by assuming that there is some predetermined play ordering for the players. While this is meaningful for ordered turn-based games, there are a host of scenarios where the players need not be constrained to make their moves in such a manner. Little research has been done for turn-based games of this kind such as financial games that involve buying and selling on the stock market in no specific order (For games with shared resources (e.g., financial games) or simultaneously-played move games, one could alternatively consider multi-player AI algorithms to be those that treat the game with each opponent as a separate game. This is currently open.). In this paper, we shall present and test a new algorithm for multi-player game playing on a game which does not require a fixed sequential play ordering. The game that we have used to demonstrate this is the multi-player Snake Game, also referred to as a “Light Bike” game which is a turn-based game requiring simultaneous moves at every turn. Our newly-proposed scheme, the Multi-Minimax, along with the Added Pruning method, performs better when compared to the similar AI strategies examined in this paper. Additionally, among all the algorithms that did not use the proposed pruning, Multi-Minimax performs the best. We can conclude that, at the least, under certain conditions in the area of multi-player game playing AI, similar results can be replicated with these newly proposed Added Pruning and Multi-Minimax methods. As far as we know, the results presented here are of a pioneering sort, and we are unaware of any comparable results.

B. J. Oommen—Chancellor’s Professor; Life Fellow: IEEE and Fellow: IAPR. This author is also an Adjunct Professor with the University of Agder in Grimstad, Norway.