A unifying learning framework for building artificial game-playing agents

NCU Institutional Repository > 總教學中心委員會 > 語言中心 > 期刊論文 > Item 987654321/107856

請使用永久網址來引用或連結此文件: https://ir.lib.ncu.edu.tw/handle/987654321/107856

題名:	A unifying learning framework for building artificial game-playing agents
作者:	陳雯玲;Chen, Wenlin;Chen, Yixin;Levine, David K.
貢獻者:	總教學中心語言中心
關鍵詞:	Agents (artificial intelligence);Algorithms;Artificial Intelligence;Bayesian analysis;Buildings;Complex Systems;Computer Science;Construction;Construction costs;Economics;Games;Learning;Machine learning;Mathematical models;Mathematics
日期:	2015-01-31
上傳時間:	2026-04-23 14:27:27 (UTC+8)
出版者:	Springer Netherlands;Cham: Springer International Publishing
摘要:	摘要： This paper investigates learning-based agents that are capable of mimicking human behavior in game playing, a central task in computational economics. Although computational economists have developed various game-playing agents, well-established machine learning methods such as graphical models have not been applied before. Leveraging probabilistic graphical models, this paper presents a novel sequential Bayesian network (SBN) framework for building artificial game-playing agents. We show that many existing agents, including reinforcement learning, fictitious play, and many of their variants, have a unified Bayesian explanation within the proposed SBN framework. Moreover, we discover that SBN can handle various important settings of game playing, allowing for a broad scope of its use in economics. SBN not only provides a unifying and satisfying framework to explain existing learning approaches in virtual economies, but also enables the development of new algorithms that are stronger or have fewer restrictions. In this paper, we derive a new algorithm, Hidden Markovian Play (HMP), from the generic SBN model to handle an important but difficult setting in which a player cannot observe the opponent’s strategy and payoff. It leverages Markovian learning to infer unobservable information, leading to higher quality of the agents. Experiments on real-world field experiments in evaluating economies show that our HMP model outperforms the baseline algorithms for building artificial agents. 其他題名： Ann Math Artif Intell 出版者： Cham: Springer International Publishing 出版日期： 2015-04-01 出處： Annals of mathematics and artificial intelligence, 2015-04, Vol.73 (3-4), p.335-358 版權： Springer International Publishing Switzerland 2015 版權： Springer International Publishing Switzerland 2015. 識別號： ISSN: 1012-2443 識別號： EISSN: 1573-7470 識別號： DOI: 10.1007/s10472-015-9450-1
顯示於類別:	[語言中心] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	30	檢視/開啟

在NCUIR中所有的資料項目都受到原著作權保護.

社群 sharing

資料載入中.....