Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning

2026-02-25

AI Summary

Existing task planning approaches suffer from limited efficiency and interpretability in detecting goal unreachability, explaining infeasibility causes, and adapting to dynamic changes in goals or constraints. This work proposes a planning framework based on relaxed Petri net reachability, integrating incremental constraint solving with invariant synthesis to enable efficient detection of infeasible plans and provide interpretable feedback, while supporting dynamic updates in sequential task planning. Experimental results demonstrate that the proposed method identifies up to twice as many infeasible scenarios as baseline approaches, generates a comparable number of invariants, matches baseline performance in single-shot planning, and significantly outperforms existing methods in sequential planning scenarios involving dynamic updates.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• infeasibility explanation
• sequential task planning
• plan adaptation
• goal unreachability
• planning domain update

Innovation

• Petri net relaxation
• infeasibility explanation
• sequential task planning
• incremental constraint solving
• goal-unreachability detection