Practical Decision-Theorectic Planning

IRI-9509165威斯康星州-密尔沃基的彼得·哈达维大学$78，038 - 12个月。 实用决策理论规划 解决实际现实世界规划问题的愿望对独立于域的规划系统提出了相互矛盾的要求。 规划器使用的表示必须足够丰富，以捕获应用程序的所有突出方面，但同时，它必须允许使用有效的算法，这些算法将有效地扩展以处理大型问题。 在不确定性下的规划中，决策理论允许在部分可满足的目标之间进行权衡。 但大多数规划算法要么做了不切实际的假设，要么效率太低，无法应用于大型问题。 本研究的目的是开发有效的方法，通过使用抽象技术，允许规划者在考虑细节之前专注于一个领域的重要方面。 自动生成抽象的方法正在开发中，沿着的还有将这些方法应用于动态环境中的规划问题的方法。 决策理论计划者正在扩展他们的代表能力，目标是不牺牲效率。利用概率域理论进行有效推理的技术正在开发中。 有形的产品预计将是一个有效的决策理论规划的理论框架，一个或更多的实施规划系统，一个理论框架的概率域建模使用概率逻辑，并实现推理系统构建贝叶斯网络的概率逻辑句子的知识库。

IRI-9509165 Peter Haddawy University of Wisconsin - Milwaukee $78,038 - 12 mos. Practical Decision-Theoretic Planning The desire to solve practical real-world planning problems places conflicting demands on domain-independent planning systems. The representation used by the planner must be rich enough to capture all the salient aspects of the application, but at the same time, it must permit the use of efficient algorithms that will scale up effectively to handle large problems. In planning under uncertainty, decision theory allows the consideration of tradeoffs among partially satisfiable objectives. But most of the planning algorithms have either made unrealistic assumptions or been too inefficient for application to large problems. The purpose of this research is to develop efficient methods by using abstraction techniques which permit a planner to focus on important aspects of a domain before considering details. Methods of automatically generating abstractions are being developed, along with methods of applying these to planning problems in dynamic environments. Decision-theoretic planners are being extended ion their representational capabilities with the goal of not sacrificing efficiency. Techniques for efficient reasoning with probabilistic domain theories are being developed. The tangible products are expected to be a theoretical framework for efficient decision-theoretic planning, ore or more implemented planning systems, a theoretical framework for probabilistic domain modeling using probability logic, and an implemented inference system for constructing Bayesian networks from knowledge bases of probability logic sentences.