Lifted Sequential Planning with Lazy Constraint Generation Solvers

TL;DR

This paper introduces a novel constraint programming approach using Lazy Clause Generation for lifted sequential planning, avoiding grounding and explicit state representation, and demonstrates competitive performance on benchmark problems.

Contribution

It proposes a new lifted CP model with lazy constraint generation that eliminates the need for grounding and explicit state encoding in planning.

Findings

Methods perform well on problems with fewer plan steps

The approach broadens feasible inference methods in planning as CSP solving

Results compare favorably with state-of-the-art in optimal sequential planning

Abstract

This paper studies the possibilities made open by the use of Lazy Clause Generation (LCG) based approaches to Constraint Programming (CP) for tackling sequential classical planning. We propose a novel CP model based on seminal ideas on so-called lifted causal encodings for planning as satisfiability, that does not require grounding, as choosing groundings for functions and action schemas becomes an integral part of the problem of designing valid plans. This encoding does not require encoding frame axioms, and does not explicitly represent states as decision variables for every plan step. We also present a propagator procedure that illustrates the possibilities of LCG to widen the kind of inference methods considered to be feasible in planning as (iterated) CSP solving. We test encodings and propagators over classic IPC and recently proposed benchmarks for lifted planning, and report that for planning problem instances requiring fewer plan steps our methods compare very well with the state-of-the-art in optimal sequential planning.