Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to
  Task Planning
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.
6860f1639e7509383d29aaba
Harisankar Babu
claimed_verified
2025-06-29T10:14:11.847Z
63d94d4e89a9683b46d559ba
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Harisankar Babu
user
harisankar95
6860f1639e7509383d29aabb
Philipp Schillinger
6860f1639e7509383d29aabc
Tamim Asfour
Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to
  Task Planning
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.
TAPAS integrates LLMs with symbolic planning to dynamically adapt and generate domain models, initial states, and goals for complex tasks, achieving strong performance in various environments and with real-world robots.
Large Language Models (LLMs) symbolic planning TAPAS multi-agent framework tool-calling mechanisms ReAct natural language plan translation