Planners from the family of Graphplan (Graphplan, IPP, STAN...) are presently considered as the most efficient ones on numerous planning domains. Their partially ordered plans can...
We examine the computational complexity of testing and nding small plans in probabilistic planning domains with both at and propositional representations. The complexity of plan e...
Michael L. Littman, Judy Goldsmith, Martin Mundhen...
Domain independent planners can produce better-quality plans through the use of domain-speci c knowledge, typically encoded as search control rules. The planning-by-rewriting appro...
This paper deals with a well known problem in AI planning: detecting and resolving conflicts in nonlinear plans. We sketch a theory of restricted conflict detection and resolution...
In this paper, a novel artificial potential function is proposed for planning the path of a robotic sensor in a partially observed environment containing multiple obstacles and mul...