The Automatic Inference of State Invariants in TIM

TL;DR

This paper presents a method for automatically inferring state invariants from domain descriptions to assist in debugging and improve the efficiency of domain-independent planners like STAN, a Graphplan-based system.

Contribution

It introduces a novel process for extracting state invariants from the type structure of a domain, aiding both domain debugging and planner performance enhancement.

Findings

Inferred invariants help identify domain errors.

Invariants improve planner efficiency.

Method integrates with the STAN planner.

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.