The Automatic Inference of State Invariants in TIM

Abstract

As planning is applied to larger and richer domains the effort involved in constructing domain descriptions increases and becomes a significant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with some assistance in building correctly encoded domains. One way of doing this is to provide domain-independent techniques for extracting, from a domain description, knowledge that is implicit in that description and that can assist domain designers in debugging domain descriptions. This knowledge can also be exploited to improve the performance of planners: several researchers have explored the potential of state invariants in speeding up the performance of domain-independent planners. In this paper we describe a process by which state invariants can be extracted from the automatically inferred type structure of a domain. These techniques are being developed for exploitation by STAN, a Graphplan based planner that employs state analysis techniques to enhance its performance.

References in corpus (1)

• Efficient Implementation of the Plan Graph in STAN

Cited by in corpus (7)

• The FF Planning System: Fast Plan Generation Through Heuristic Search
• Planning Through Stochastic Local Search and Temporal Action Graphs in LPG
• On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm
• Efficient Implementation of the Plan Graph in STAN
• Planning by Rewriting
• The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
• Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems