Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.artint.2017.12.004 | Artificial Intelligence |
Keywords | Field | DocType |
AI planning,Heuristic search,Problem decomposition | Topology,Discrete mathematics,Search algorithm,Star network,Conditional independence,Reachability,State space search,State variable,State space,Mathematics,A* search algorithm | Journal |
Volume | Issue | ISSN |
257 | 1 | 0004-3702 |
Citations | PageRank | References |
0 | 0.34 | 65 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Gnad | 1 | 12 | 6.89 |
| Jörg Hoffmann | 2 | 2702 | 189.88 |