用佩特里网松弛法检测计划不可行性并支持动态更新
Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning
- 通过佩特里网可达性松弛实现鲁棒不变量合成
- 检测到的不可行性比基线多2倍，且支持约束增量更新
- 适合需要动态调整计划的复杂任务系统
计划常因情境变化或认知调整而失效。某些情况下可行计划根本不存在，识别此类不可行性有助于判断需求是否需修正。现有规划方法多聚焦于可行情况下的高效一次性规划，缺乏对领域更新和不可行性检测的支持。本文提出一种佩特里网可达性松弛方法，实现鲁棒不变量合成、高效目标不可达检测及有意义的不可行性解释。进一步利用增量约束求解器支持目标与约束的动态更新。实验表明，相比基线方法，本系统生成的不变量数量相当，检测到的不可行性最多提升2倍，在一次性规划中表现相当，而在序列化计划更新中显著更优。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。