Markov Decision Processes and the Belief-Desire-Intention Model - Bridging the Gap for Autonomous Agents
Markov Decision Processes and the Belief-Desire-Intention Model - Bridging the Gap for Autonomous Agents
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马尔可夫决策过程和信念-欲望-意图模型 - 弥合自治代理的差距
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
S. Parsons
中科院分区：
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作者：
Gerardo I. Simari;S. Parsons
在这项工作中，我们提供了一个治疗的两个模型之间的关系，已被广泛用于实现自主代理：信念DesireIntention（BDI）模型和马尔可夫决策过程（MDPs）。我们开始与关系的非正式描述，确定这两种方法的共同特点和它们之间的差异。然后，我们通过对TileWorld测试平台上两种模型的性能进行实证分析，来磨练我们对这些差异的理解。这使我们能够表明，即使MDP模型在小世界中始终表现出比BDI模型更好的行为，但当世界变得很大并且MDP模型无法精确求解时，情况并非如此。最后，我们提出了一个理论分析的两种方法之间的关系，确定映射，使我们能够提取一组意图从一个政策（一个解决方案的MDP），并提取一个政策从一组意图。
In this work, we provide a treatment of the relationship between two models that have been widely used in the implementation of autonomous agents: the Belief DesireIntention (BDI) model and Markov Decision Processes (MDPs). We start with an informal description of the relationship, identifying the common features of the two approaches and the differences between them. Then we hone our understanding of these differences through an empirical analysis of the performance of both models on the TileWorld testbed. This allows us to show that even though the MDP model displays consistently better behavior than the BDI model for small worlds, this is not the case when the world becomes large and the MDP model cannot be solved exactly. Finally we present a theoretical analysis of the relationship between the two approaches, identifying mappings that allow us to extract a set of intentions from a policy (a solution to an MDP), and to extract a policy from a set of intentions.