State of the Field
Recent advancements in AI planning are focusing on enhancing the robustness and applicability of planning systems in complex, multi-agent environments. The introduction of the Epistemic Planning Domain Definition Language aims to standardize the representation of epistemic planning tasks, addressing fragmentation and improving interoperability among planners. Concurrently, efforts to mitigate biases in goal recognition datasets are reshaping how planners are evaluated, with new methodologies generating diverse plans that challenge existing systems. Additionally, innovative frameworks like PathWise leverage large language models for automated heuristic design, enabling more efficient and adaptable planning processes. Meanwhile, techniques such as Petri net relaxation are being developed to better handle infeasibilities in planning, allowing for dynamic adjustments to plans as situations evolve. Collectively, these trends indicate a shift toward more flexible, efficient, and collaborative planning systems that can adapt to real-world complexities, potentially transforming applications in robotics, logistics, and autonomous systems.
Papers
1–6 of 6On Sample-Efficient Generalized Planning via Learned Transition Models
Generalized planning studies the construction of solution strategies that generalize across families of planning problems sharing a common domain model, formally defined by a transition function $γ: S...
The Epistemic Planning Domain Definition Language: Official Guideline
Epistemic planning extends (multi-agent) automated planning by making agents' knowledge and beliefs first-class aspects of the planning formalism. One of the most well-known frameworks for epistemic p...
Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation
Autonomous agents require some form of goal and plan recognition to interact in multiagent settings. Unfortunately, all existing goal recognition datasets suffer from a systematical bias induced by th...
PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs
Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks' reliance on fixed evolutionary rules and static prom...
Intermediate Results on the Complexity of STRIPS$_{1}^{1}$
This paper is based on Bylander's results on the computational complexity of propositional STRIPS planning. He showed that when only ground literals are permitted, determining plan existence is PSPACE...
Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning
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 whe...