Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks
Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks
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DOI：
10.1609/aimag.v33i4.2402
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Prashant Doshi
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文献类型：
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作者：
Prashant Doshi
决策是自治系统的一个关键特征。它涉及在各种信息环境中的不同行动路线之间进行最佳选择，这些信息范围从完全了解决策问题的所有方面到仅部分了解该问题。物理环境通常包括其他交互的自治系统，通常称为代理。在本文中，我重点关注多主体环境中的决策制定以及有关问题的部分信息。在这个复杂但现实的环境中的相关研究集中在两个互补的通用框架上，并且还引入了无数的专业化。我将分散式部分可观察马尔可夫决策过程 (Dec-POMDP) 和交互式部分可观察马尔可夫决策过程 (I-POMDP) 这两个框架放在上下文中，回顾这些框架的基础算法，同时简要讨论其专业化的进展。最后，我检查了与这些框架相关的研究正在追求的途径。
Decision making is a key feature of autonomous systems. It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations. I conclude by examining the avenues that research pertaining to these frameworks is pursuing.