摘要翻译:
通常可以通过将任务分解为分层排列的较小任务来简化规划。查林等人。[4]最近的研究表明,层次发现问题可以转化为一个非凸优化问题。然而,求解这类优化问题所固有的计算困难使得它很难扩展到现实世界中的问题。在另一个研究领域,Toussaint等人。[18]提出了一种用极大似然估计求解规划问题的方法。在本文中,我们展示了如何用类似的最大似然方法来解决部分可观察域中的层次发现问题。我们的rst技术将问题转化为一个动态贝叶斯网络,通过该网络可以自然地发现一个层次结构,同时优化策略。实验结果表明,该方法比以往基于非凸优化的方法具有更好的可扩展性。
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英文标题:
《Hierarchical POMDP Controller Optimization by Likelihood Maximization》
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作者:
Marc Toussaint, Laurent Charlin, Pascal Poupart
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最新提交年份:
2012
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
Planning can often be simpli ed by decomposing the task into smaller tasks arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy discovery problem can be framed as a non-convex optimization problem. However, the inherent computational di culty of solving such an optimization problem makes it hard to scale to realworld problems. In another line of research, Toussaint et al. [18] developed a method to solve planning problems by maximumlikelihood estimation. In this paper, we show how the hierarchy discovery problem in partially observable domains can be tackled using a similar maximum likelihood approach. Our technique rst transforms the problem into a dynamic Bayesian network through which a hierarchical structure can naturally be discovered while optimizing the policy. Experimental results demonstrate that this approach scales better than previous techniques based on non-convex optimization.
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PDF链接:
https://arxiv.org/pdf/1206.3291