3 articles tagged with #planning-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers used large language models and evolutionary search to create the first domain-independent heuristics for symbolic AI planning that surpass hand-engineered baselines. These evolved heuristics, written in C++, solve more planning tasks than existing state-of-the-art approaches and maintain the soundness guarantees of traditional planners.
Researchers introduce Thoughts-as-Planning, a novel framework that optimizes reasoning chains in large language models by modeling them as sequential decision-making processes over a latent semantic space. The method uses learned world models to simulate how edits to reasoning chains affect outputs, enabling efficient planning through gradient descent or reinforcement learning while supporting multi-scale abstraction across token, segment, and instruction levels.
Researchers introduce PlanAhead, a framework that systematically evaluates how different natural language plan representations affect LLM-based web agent performance across multiple AI models. The study finds that both the plan formulation method and underlying LLM significantly impact agent robustness, with implications for improving autonomous AI systems that interact with web interfaces.
