#pddl2 articles

2 articles

AIBullisharXiv – CS AI · Feb 277/106

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Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning

Researchers developed a hierarchical multi-agent LLM framework that significantly improves multi-robot task planning by combining natural language processing with classical PDDL planners. The system uses prompt optimization and meta-learning to achieve success rates of up to 95% on compound tasks, outperforming previous state-of-the-art methods by substantial margins.

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AINeutralarXiv – CS AI · 17h ago4/10

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Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation

Researchers developed PyPDDLEngine, an open-source tool that allows large language models to perform task planning through interactive PDDL simulation. Testing on 102 planning problems showed agentic LLM planning achieved 66.7% success versus 63.7% for direct LLM planning, but at 5.7x higher token cost, while classical planning methods reached 85.3% success.

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