Using Game AI to Control a Simulated Economy

被引:0
作者
McCarlie, Paul [1 ]
Hunter, Aaron [1 ]
机构
[1] British Columbia Inst Technol, 3700 Willingdon Ave, Burnaby, BC, Canada
来源
ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2 | 2021年
关键词
AI for Games; Rule-based Systems; Machine Learning;
D O I
10.5220/0010212306290634
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We explore the use of Artificial Intelligence (AI) to manipulate a simulated economy. Towards this end, we present work in progress on a macroeconomic simulation that can be controlled by a game player. We view this simulation as a sort of serious game; it can be played as a competition, but it can also be an educational tool through which players learn both about economic principles and the behaviour of AI controllers. The main contribution of this paper is the comparative study of the effectiveness of different AI agents for the manipulation of a simulated environment. Focusing on AI approaches that are common in the gaming industry, we implement four players that use intelligent methods to control the simulation by trying to maximize the economic output. The aim of our work is to illustrate that simple methods from the game AI community can be used to control a complex economic simulation effectively. This work, therefore, supports the common position in the gaming community that simple character-based AI methods can produce competitive game play even for complex tasks. Moreover, we demonstrate that, in the case of this particular simulation, a rule-based reasoner outperforms more sophisticated AI agents.
引用
收藏
页码:629 / 634
页数:6
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