TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models

2024-03-25

arXiv.org

AI Summary

To address the challenge of balancing concurrent action conflicts and plan executability in multi-agent task planning, this paper proposes a two-stage LLM-PDDL collaborative framework. First, a large language model (LLM) performs commonsense-driven goal decomposition to generate mutually exclusive, parallelizable sub-goals. Second, a classical PDDL planner (e.g., FF or Fast Downward) independently synthesizes formally verifiable single-agent plans for each agent. This work is the first to integrate the LLM’s high-level goal abstraction capability with the formal correctness guarantees of classical planning. Empirical results demonstrate 100% action executability, significantly reduced planning time, and plan step counts that outperform single-agent baselines while approaching human expert performance—thereby unifying efficiency, feasibility, and coordination quality in multi-agent planning.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, for example that two agents in the domain can execute an action simultaneously if postconditions of each do not interfere with preconditions of the other. A human expert can decompose a goal into largely independent constituent parts and assign each agent to one of these subgoals to take advantage of simultaneous actions for faster execution of plan steps, each using only single agent planning. By contrast, large language models (LLMs) used for directly inferring plan steps do not guarantee execution success, but do leverage commonsense reasoning to assemble action sequences. We combine the strengths of classical planning and LLMs by approximating human intuitions for two-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success. Additionally, we find that LLM-based approximations of subgoals can achieve similar multi-agent execution steps than those specified by human experts. Website and resources at https://glamor-usc.github.io/twostep

Problem

• Combining classical planning and LLMs for multi-agent task decomposition
• Improving planning time and execution steps in multi-agent PDDL
• Matching human expert subgoal performance with LLM approximations

Innovation

• Combines classical planning with LLMs
• Uses LLM for multi-agent goal decomposition
• Ensures execution success with fewer steps