Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning

TL;DR

This paper introduces a Petri net relaxation method to improve infeasibility detection and explanation in planning, supporting dynamic updates and outperforming baselines in sequential planning scenarios.

Contribution

It proposes a Petri net reachability relaxation technique combined with incremental constraint solving for better infeasibility analysis and plan updates.

Findings

Detects up to 2 times more infeasibilities than baselines

Performs competitively in one-shot planning

Outperforms in sequential plan updates

Abstract

Plans often change due to changes in the situation or our understanding of the situation. Sometimes, a feasible plan may not even exist, and identifying such infeasibilities is useful to determine when requirements need adjustment. Common planning approaches focus on efficient one-shot planning in feasible cases rather than updating domains or detecting infeasibility. We propose a Petri net reachability relaxation to enable robust invariant synthesis, efficient goal-unreachability detection, and helpful infeasibility explanations. We further leverage incremental constraint solvers to support goal and constraint updates. Empirically, compared to baselines, our system produces a comparable number of invariants, detects up to 2 times more infeasibilities, performs competitively in one-shot planning, and outperforms in sequential plan updates in the tested domains.