#pddl News & Analysis

6 articles tagged with #pddl. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Apr 77/10

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Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation

Researchers have developed a neuro-symbolic framework that enables robots to learn complex manipulation tasks from as few as one demonstration, without requiring manual programming or large datasets. The system uses Vision-Language Models to automatically construct symbolic planning domains and has been validated on real industrial equipment including forklifts and robotic arms.

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 · Mar 266/10

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DUPLEX: Agentic Dual-System Planning via LLM-Driven Information Extraction

Researchers propose DUPLEX, a dual-system architecture that restricts LLMs to information extraction rather than end-to-end planning, using symbolic planners for logical synthesis. The system demonstrated superior performance across 12 planning domains by leveraging LLMs for semantic grounding while avoiding their hallucination tendencies in complex reasoning tasks.

AINeutralarXiv – CS AI · Mar 176/10

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Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective

Researchers propose a hierarchical planning framework to analyze why LLM-based web agents fail at complex navigation tasks. The study reveals that while structured PDDL plans outperform natural language plans, low-level execution and perceptual grounding remain the primary bottlenecks rather than high-level reasoning.

AIBullisharXiv – CS AI · Mar 115/10

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GenePlan: Evolving Better Generalized PDDL Plans using Large Language Models

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

AINeutralarXiv – CS AI · Mar 94/10

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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.

🧠 Claude🧠 Haiku