Star-topology decoupled state space search

作者:

摘要

State space search is a basic method for analyzing reachability in discrete transition systems. To tackle large compactly described transition systems – the state space explosion – a wealth of techniques (e.g., partial-order reduction) have been developed that reduce the search space without affecting the existence of (optimal) solution paths. Focusing on classical AI planning, where the compact description is in terms of a vector of state variables, an initial state, a goal condition, and a set of actions, we add another technique, that we baptize star-topology decoupling, into this arsenal. A star topology partitions the state variables into components so that a single center component directly interacts with several leaf components, but the leaves interact only via the center. Many applications explicitly come with such structure; any classical planning task can be viewed in this way by selecting the center as a subset of state variables separating connected leaf components.

论文关键词:AI planning,Heuristic search,Problem decomposition

论文评审过程:Received 3 May 2017, Revised 20 December 2017, Accepted 21 December 2017, Available online 27 December 2017, Version of Record 9 January 2018.

论文官网地址:https://doi.org/10.1016/j.artint.2017.12.004