论文标题
迭代深度第一搜索，以完全可观察到的非确定性计划
Iterative Depth-First Search for Fully Observable Non-Deterministic Planning
论文作者
论文摘要
完全可观察到的非确定性（FONT）计划通过具有非确定效应的行动模型不确定性。现有的FOND计划算法有效，并采用了广泛的技术。但是，大多数现有算法对于处理非确定性和任务规模并不强大。在本文中，我们开发了一种新型的迭代深度优先搜索算法，该算法解决了精心的计划任务并产生强大的环状政策。我们的算法是针对精心计划的明确设计的，更直接地解决了FOND计划的非确定性方面，并且还利用了启发式功能的好处，以使算法在迭代搜索过程中更有效。我们将提出的算法与众所周知的Fond Planners进行了比较，并表明考虑到不同的指标，它在几种不同类型的FOND领域具有良好的性能。
Fully Observable Non-Deterministic (FOND) planning models uncertainty through actions with non-deterministic effects. Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process. We compare our proposed algorithm to well-known FOND planners, and show that it has robust performance over several distinct types of FOND domains considering different metrics.
