A Constraint-based Encoding for Domain-Independent Temporal Planning

TL;DR

This paper introduces a new constraint-based encoding for domain-independent temporal planning that uses a lifted, template-level representation, resulting in more efficient planning solutions when solved with SMT solvers.

Contribution

The paper presents a novel, fully lifted, time-oriented encoding for task planning that improves efficiency over existing constraint-based methods.

Findings

Encoding is more efficient than state-of-the-art methods.

Encoding outperforms other fully constraint-based approaches.

Growth of encoding is proportional to plan actions, not primitive actions.

Abstract

We present a general constraint-based encoding for domain-independent task planning. Task planning is characterized by causal relationships expressed as conditions and effects of optional actions. Possible actions are typically represented by templates, where each template can be instantiated into a number of primitive actions. While most previous work for domain-independent task planning has focused on primitive actions in a state-oriented view, our encoding uses a fully lifted representation at the level of action templates. It follows a time-oriented view in the spirit of previous work in constraint-based scheduling. As a result, the proposed encoding is simple and compact as it grows with the number of actions in a solution plan rather than the number of possible primitive actions. When solved with an SMT solver, we show that the proposed encoding is slightly more efficient than state-of-the-art methods on temporally constrained planning benchmarks while clearly outperforming other fully constraint-based approaches.