Recent advancements in AI planning are increasingly focused on enhancing the efficiency and adaptability of planning systems. Notably, the integration of learned transition models is showing promise in improving sample efficiency and generalization across diverse planning problems, suggesting a shift away from traditional symbolic methods. This approach allows planners to predict intermediate world states, potentially reducing the data requirements for training. Concurrently, efforts to mitigate biases in goal recognition datasets are paving the way for more robust evaluations of multi-agent interactions, which is crucial for real-world applications. The introduction of a standardized language for epistemic planning is also addressing fragmentation in the field, facilitating better comparisons and interoperability among planners. Additionally, novel frameworks that leverage large language models for automated heuristic design are transforming how heuristics are generated, leading to faster convergence and improved performance in complex optimization tasks. Collectively, these developments indicate a maturation of the field, with a clear focus on practical applications and real-world problem-solving.
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...
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...
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...
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...
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...
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...