Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to
  Task Planning
Authors: name: Harisankar Babu, name: Philipp Schillinger, name: Tamim Asfour

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.