Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning

• URL: http://arxiv.org/abs/2506.19592v2
• Date: Mon, 30 Jun 2025 14:40:24 GMT
• Title: Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning
• Authors: Harisankar Babu, Philipp Schillinger, Tamim Asfour,
• Abstract summary: TAPAS employs specialized LLM-based agents that collaboratively generate and adapt domain models.<n>A ReAct (Reason+Act)-style execution agent, coupled with natural language plan translation, bridges the gap between dynamically generated plans and real-world robot capabilities.
• Score: 5.638621244710438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce TAPAS (Task-based Adaptation and Planning using AgentS), a multi-agent framework that integrates Large Language Models (LLMs) with symbolic planning to solve complex tasks without the need for manually defined environment models. TAPAS employs specialized LLM-based agents that collaboratively generate and adapt domain models, initial states, and goal specifications as needed using structured tool-calling mechanisms. Through this tool-based interaction, downstream agents can request modifications from upstream agents, enabling adaptation to novel attributes and constraints without manual domain redefinition. A ReAct (Reason+Act)-style execution agent, coupled with natural language plan translation, bridges the gap between dynamically generated plans and real-world robot capabilities. TAPAS demonstrates strong performance in benchmark planning domains and in the VirtualHome simulated real-world environment.

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