Epsilon-Subjective Equivalence of Models for Interactive Dynamic Influence Diagrams
Epsilon-Subjective Equivalence of Models for Interactive Dynamic Influence Diagrams
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DOI：
10.1109/wi-iat.2010.74
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发表时间：
2010-08
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影响因子：
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通讯作者：
Prashant Doshi;Muthukumaran Chandrasekaran;Yi-feng Zeng
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文献类型：
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作者：
Prashant Doshi;Muthukumaran Chandrasekaran;Yi-feng Zeng
交互式动态影响图（I-DID）是在不确定环境中由其他代理共享的顺序决策的图形模型。随着时间的推移，用于解决I-DID的算法面临着指数增长的归因于其他代理的候选模型空间的挑战。修剪行为等价模型是最小化模型集的一种方法。我们试图通过额外修剪主观上近似等价的模型来进一步降低复杂性。为此，我们定义主观等价的分布在主体代理的未来行动观察路径，并引入ε主观等价的概念。我们提出了一种新的近似技术，减少了候选模型空间，通过删除模型，ε-主观等效的代表。
Interactive dynamic influence diagrams (I-DID) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Pruning behaviorally equivalent models is one way toward minimizing the model set. We seek to further reduce the complexity by additionally pruning models that are approximately subjectively equivalent. Toward this, we define subjective equivalence in terms of the distribution over the subject agent's future action-observation paths, and introduce the notion of epsilon-subjective equivalence. We present a new approximation technique that reduces the candidate model space by removing models that are epsilon-subjectively equivalent with representative ones.