9 papers
Planning in the LLM Era: Building for Reliability and Efficiency
Michael Katz, Harsha Kokel, Kavitha Srinivas +1
Growing attention to intelligent agents has put a spotlight on one of their central capabilities: planning. Early attempts to leverage large language models (LLMs) for planning rel…
Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents
Shirin Sohrabi, Haritha Ananthakrishnan, Harsha Kokel +2
We present a dynamic policy-learning approach that combines generalized planning and hierarchical task decomposition for LLM-based agents. Our method, Hierarchical Component Learni…
Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
James Oswald, Daniel Obolensky, Volodymyr Varha +5
The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work sugges…
ACPBench Hard: Unrestrained Reasoning about Action, Change, and Planning
Harsha Kokel, Michael Katz, Kavitha Srinivas +1
The ACPBench dataset provides atomic reasoning tasks required for efficient planning. The dataset is aimed at distilling the complex plan generation task into separate atomic reaso…
ACPBench: Reasoning about Action, Change, and Planning
Harsha Kokel, Michael Katz, Kavitha Srinivas +1
There is an increasing body of work using Large Language Models (LLMs) as agents for orchestrating workflows and making decisions in domains that require planning and multi-step re…
Thought of Search: Planning with Language Models Through The Lens of Efficiency
Michael Katz, Harsha Kokel, Kavitha Srinivas +1
Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for t…