Robust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks

سال انتشار: 1398
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 7، شماره: 1
کد COI اختصاصی: JR_JADM-7-1_013
زبان مقاله: انگلیسی
تعداد مشاهده: 376

نویسندگان

Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran.

Amirkabir University of Technology

چکیده

Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope with the uncertainty existing in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-logs dataset; so it does not require a human expert’s knowledge. We evaluate our model on StarCraft which is considered as a unified test-bed in this domain. The model is compared with that proposed by Synnaeve and Bessiere. Experimental results on recorded games of human players show that the proposed model can predict the opponent’s future decisions more effectively. Using this model, it is possible to create an adaptive game intelligence algorithm applicable to RTS games, where the concept of build order (the order of building construction) exists.

کلیدواژه ها

Bayesian Network, Opponent modeling, Real-Time Strategy games, StarCraft

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