Planning by Probabilistic Inference
Planning by Probabilistic Inference
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发表时间：
2003
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通讯作者：
H. Attias
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作者：
H. Attias
本文提出并论证了一种解决不确定条件下规划问题的新方法。在涉及动作和状态的概率生成模型中，动作被视为隐变量，具有自己的先验分布。计划是通过计算行动的后验分布来完成的，条件是在指定的步骤数内达到目标状态。在新的表述下，将推理技术的工具箱引入规划问题。本文主要研究具有离散动作和状态的问题，并讨论了一些扩展。
This paper presents and demonstrates a new approach to the problem of planning under uncertainty. Actions are treated as hidden variables, with their own prior distributions, in a probabilistic generative model involving actions and states. Planning is done by computing the posterior distribution over actions, conditioned on reaching the goal state within a specified number of steps. Under the new formulation, the toolbox of inference techniques be brought to bear on the planning problem. This paper focuses on problems with discrete actions and states, and discusses some extensions.