AIBearisharXiv – CS AI · 4d ago6/10
🧠A complementary study of PlanGPT, an LLM-based automated planning system, challenges its effectiveness by re-evaluating its performance against traditional planners using metrics like plan cost and generation time. The research questions whether planning with large language models is truly beneficial, finding that PlanGPT performs no better than basic greedy search strategies.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers introduce SPG-LLM, a novel approach that leverages large language models to optimize the grounding process in classical planning by identifying irrelevant objects and actions before computation. The method achieves significantly faster grounding times—often by orders of magnitude—across seven challenging benchmarks while maintaining or improving plan quality.
AIBullisharXiv – CS AI · Jun 26/10
🧠Researchers have developed an automated method to generate PDDL planning problems directly from Asset Administration Shell (AAS) capability models using Industry 4.0 standards, eliminating the need for specialized planning expertise. This approach enables production engineers to design and verify manufacturing system layouts without requiring knowledge of formal planning languages, significantly reducing barriers to adopting automated planning in industrial settings.
AINeutralarXiv – CS AI · Jun 16/10
🧠Researchers propose novel methods for encoding factored tasks—a compact planning representation—into SAT (Boolean satisfiability) problems, moving beyond traditional heuristic search approaches. The work examines multiple encoding strategies and analyzes how task transformations and parallelism affect SAT-based planner performance, advancing computational planning techniques.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers propose a unified framework for understanding Tree-of-Thoughts (ToT) as a classical heuristic search problem, mapping LLM reasoning to established search algorithms. The work synthesizes fragmented research across NLP and planning communities, identifying design patterns where Best-First Search suits shallow tasks while deeper reasoning benefits from lookahead-heavy strategies like DFS and MCTS.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce DSAT, a native SAT solver designed to work directly with discrete variables rather than converting them to binary Boolean variables. The solver applies traditional SAT techniques like unit resolution and clause learning to discrete logic, offering potential computational and semantic advantages over existing binarization approaches for applications in probabilistic reasoning, planning, and explainable AI.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers have developed a parallel lifted planning algorithm using semi-naive Datalog evaluation that significantly accelerates classical AI planning by combining rule-level and grounding-level parallelism. The approach achieves up to 6-fold speedup on 8 cores and solves more planning tasks than existing baselines, particularly on computationally intensive grounding operations.
AIBullisharXiv – CS AI · May 116/10
🧠Researchers demonstrate that large language models can generate effective heuristics for hierarchical task network (HTN) planning, achieving near-optimal performance compared to state-of-the-art planners. LLM-generated heuristics reduce search effort on 83% of benchmark problems, suggesting AI models can enhance algorithmic planning efficiency beyond classical approaches.
AINeutralarXiv – CS AI · Apr 146/10
🧠A new thesis examines explainable AI planning (XAIP) for hybrid systems, addressing the critical challenge of making autonomous planning decisions interpretable in safety-critical applications. As AI automation expands into domains like autonomous vehicles, energy grids, and healthcare, the ability to explain system reasoning becomes essential for trust and regulatory compliance.
AIBullisharXiv – CS AI · Mar 115/10
🧠Researchers present GenePlan, a framework that uses large language models with evolutionary algorithms to generate domain-specific planners for classical planning tasks in PDDL. The system achieved a 0.91 SAT score across eight benchmark domains, nearly matching state-of-the-art performance while significantly outperforming other LLM-based approaches.
🧠 GPT-4
