Planning as Tabled Logic Programming

TL;DR

This paper presents Picat's planner, which uses tabling in logic programming to efficiently solve planning problems, demonstrating its effectiveness through models for various domains in the IPC 2014 competition.

Contribution

It introduces a planning approach based on tabling in logic programming, highlighting modeling techniques and resource-bounded search strategies for improved planning performance.

Findings

Effective resource-bounded search using tabling.

Modeling techniques improve planning efficiency.

Demonstrated success on IPC 2014 domains.

Abstract

This paper describes Picat's planner, its implementation, and planning models for several domains used in International Planning Competition (IPC) 2014. Picat's planner is implemented by use of tabling. During search, every state encountered is tabled, and tabled states are used to effectively perform resource-bounded search. In Picat, structured data can be used to avoid enumerating all possible permutations of objects, and term sharing is used to avoid duplication of common state data. This paper presents several modeling techniques through the example models, ranging from designing state representations to facilitate data sharing and symmetry breaking, encoding actions with operations for efficient precondition checking and state updating, to incorporating domain knowledge and heuristics. Broadly, this paper demonstrates the effectiveness of tabled logic programming for planning, and argues the importance of modeling despite recent significant progress in domain-independent PDDL planners.