A Formal Representation for Intelligent Decision-Making in Games

Chanjuan Liu, Ruining Zhang, Yu Zhang and Enqiang Zhu ()
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Chanjuan Liu: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Ruining Zhang: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Yu Zhang: Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
Enqiang Zhu: Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China

Mathematics, 2023, vol. 11, issue 22, 1-11

Abstract: The study of intelligent game-playing has gained tremendous attention in the past few decades. The recent development of artificial intelligence (AI) players (e.g., the Go player AlphaGo) has made intelligent game-playing even more prominent in both academia and industry. The performance of state-of-the-art AI players benefits greatly from machine learning techniques, based on which, players can make estimations and decisions even without understanding the games. Although AI machines show great superiority over humans in terms of data processing and complex computation, there remains a vast distance between artificial intelligence and human intelligence with respect to the abilities of context understanding and reasoning. In this paper, we explore the theoretical foundation of intelligent game-playing from a logical perspective. The proposed logic, by considering the computational limits in practical game-playing, drops the ideal assumptions in existing logics for the classical game model. We show that under logical framework, the basis of decision-making for agents in game scenarios can be formally represented and analyzed. Moreover, by characterizing the solutions of games, this logic is able to formalize players’ rational decision-making during practical game-playing.

Keywords: decision-making; rationality; intelligent game-playing; logic; knowledge (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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