First-Order Open-Universe POMDPs: Formulation and Algorithms
First-Order Open-Universe POMDPs: Formulation and Algorithms
复制标题
一阶开放宇宙 POMDP：公式和算法
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Avi Pfeffer
中科院分区：
文献类型：
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作者：
Siddharth Srivastava;Xiang Cheng;Stuart J. Russell;Avi Pfeffer
摘要：近年来，人们对概率模型的关系语言和一阶语言的兴趣迅速增长，随之而来的是将这种语言扩展到处理完全和部分可观察的决策过程的可能性。我们研究了扩展一阶、开放宇宙语言来描述POMDP的问题，并在描述代理S的观察能力和行动问题时识别了非平凡的代表性问题，这些问题在以前的工作中只有通过做出强有力的限制性假设才能避免。我们提出了一种表示动作和观察的方法，该方法尊重可供代理使用的传感器和执行器的形式规范，并展示了如何处理以前无法表示的情况，如看到对象和拾取对象。最后，我们认为在许多情况下，开放宇宙POMDP需要信念状态策略，而不是自动机策略。我们给出了一种算法和实验结果来评估这种策略的开放-逆反POMDP。
Abstract : Interest in relational and first-order languages for probability models has grown rapidly in recent years, and with it the possibility of extending such languages to handle decision processes both fully and partially observable. We examine the problem of extending a first-order, open-universe language to describe POMDPs and identify non-trivial representational issues in describing an agent s capability for observation and action issues that were avoided in previous work only by making strong and restrictive assumptions. We present a method for representing actions and observations that respects formal specifications of the sensors and actuators available to an agent, and show how to handle cases such as seeing an object and picking it up that could not previously be represented. Finally, we argue that in many cases open-universe POMDPs require belief-state policies rather than automata policies. We present an algorithm and experimental results for evaluating such policies for open-unverse POMDPs.