Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning
arXiv:2602.22094v1 Announce Type: new 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.
arXiv:2602.22094v1 Announce Type: new 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.
Executive Summary
This article proposes a novel approach to planning using Petri net relaxation, enabling robust invariant synthesis, efficient goal-unreachability detection, and helpful infeasibility explanations. The system leverages incremental constraint solvers to support goal and constraint updates, outperforming baselines in sequential plan updates and detecting more infeasibilities. Empirical results demonstrate the effectiveness of the proposed system, which has significant implications for planning and decision-making in dynamic environments.
Key Points
- ▸ Petri net reachability relaxation for invariant synthesis and goal-unreachability detection
- ▸ Incremental constraint solvers for efficient goal and constraint updates
- ▸ Empirical evaluation demonstrating improved performance in sequential plan updates and infeasibility detection
Merits
Improved Infeasibility Detection
The proposed system detects up to 2 times more infeasibilities compared to baselines, enabling more effective planning and decision-making
Robust Invariant Synthesis
The Petri net relaxation approach enables robust invariant synthesis, supporting more reliable planning and decision-making
Demerits
Limited Domain Applicability
The empirical evaluation is limited to specific domains, and the generalizability of the proposed system to other domains is unclear
Expert Commentary
The proposed Petri net relaxation approach represents a significant advancement in planning and decision-making research. By enabling robust invariant synthesis and efficient goal-unreachability detection, the system provides a more comprehensive and effective framework for planning in dynamic environments. The empirical evaluation demonstrates the system's potential, particularly in sequential plan updates and infeasibility detection. However, further research is needed to fully explore the system's applicability and generalizability to other domains.
Recommendations
- ✓ Further empirical evaluation to assess the system's performance in diverse domains and applications
- ✓ Investigation of potential integrations with other planning and decision-making approaches to enhance overall system effectiveness
