![[2602.22094] Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22094] Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning
Summary
This paper presents a novel approach using Petri nets to identify infeasibilities in sequential task planning, enhancing robustness and efficiency in planning systems.
Why It Matters
Understanding infeasibilities in task planning is crucial for adapting to changing requirements and improving the reliability of planning systems. This research contributes to the field of artificial intelligence by providing a method that not only detects infeasibilities but also supports efficient updates to planning constraints, which is essential for dynamic environments.
Key Takeaways
- Introduces Petri net reachability relaxation for robust planning.
- Enhances detection of infeasibilities compared to traditional methods.
- Supports incremental updates to goals and constraints effectively.
- Empirical results show competitive performance in planning tasks.
- Addresses the need for adaptive planning in dynamic environments.
Computer Science > Artificial Intelligence arXiv:2602.22094 (cs) [Submitted on 25 Feb 2026] Title:Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning Authors:Nguyen Cong Nhat Le, John G. Rogers, Claire N. Bonial, Neil T. Dantam View a PDF of the paper titled Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning, by Nguyen Cong Nhat Le and 3 other authors View PDF HTML (experimental) 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. Comments: Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.8 Cite as: arXiv:2602.22094 [cs.AI] (or arXiv:2602.22094v1 [cs.AI] for this version) https://doi.org/...