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Integrating Language Models and PDDL Planners for Efficient Multi-Agent Task Allocation and Long-Horizon Planning


Core Concepts
LaMMA-P, a novel framework that integrates the reasoning ability of large language models (LLMs) with the heuristic planning algorithms of PDDL planners, achieves state-of-the-art performance on long-horizon multi-agent tasks by enabling effective task decomposition, efficient sub-task allocation, and seamless plan generation and execution.
Abstract

The paper introduces LaMMA-P, a Language Model-Driven Multi-Agent PDDL Planner, which addresses the challenge of long-horizon task planning and execution in heterogeneous multi-robot systems.

Key highlights:

  • LaMMA-P integrates the strengths of LLMs' natural language understanding and PDDL-based heuristic search planning to enable effective task decomposition, efficient sub-task allocation, and seamless plan generation and execution.
  • The framework consists of six key modules: Precondition Identifier, Task Allocator, Problem Generator, PDDL Validator, Fast Downward/LLM Planner, and Sub-Plan Combiner. These modules work together to translate high-level natural language instructions into executable plans for a team of robots.
  • The authors create MAT-THOR, a comprehensive benchmark based on the AI2-THOR environment, to evaluate the performance of multi-agent planning methods on long-horizon tasks with varying levels of complexity.
  • Experimental results show that LaMMA-P achieves a 105% higher success rate and 36% higher efficiency compared to existing LM-based multi-agent planners on the MAT-THOR benchmark.
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Stats
The paper reports the following key metrics: Success Rate (SR): LaMMA-P (GPT-4o) achieves 93% SR on Compound tasks, 77% on Complex tasks, and 45% on Vague Command tasks, outperforming the strongest baseline SMART-LLM (GPT-4o) by 105%. Efficiency (Eff): LaMMA-P (GPT-4o) achieves 90% Eff on Compound tasks, 67% on Complex tasks, and 65% on Vague Command tasks, a 36% improvement over SMART-LLM (GPT-4o).
Quotes
"LaMMA-P integrates the strengths of the LMs' reasoning capability and the traditional heuristic search planner to achieve a high success rate and efficiency while demonstrating strong generalization across tasks." "The experimental results demonstrate that LaMMA-P achieves a 105% higher success rate and 36% higher efficiency than existing LM-based multi-agent planners."

Deeper Inquiries

How can LaMMA-P be extended to handle partially observable environments and dynamic changes during task execution?

To extend LaMMA-P for partially observable environments and dynamic changes, several strategies can be implemented. First, incorporating perception capabilities through vision-language models can enhance the system's ability to interpret and react to real-time environmental changes. This would allow robots to gather information about their surroundings, updating their internal state and plans based on new observations. Second, implementing a dynamic re-planning mechanism is crucial. This could involve integrating a feedback loop where robots continuously assess their current state against the planned actions. If discrepancies arise due to unobserved changes or unexpected obstacles, the system can trigger a re-evaluation of the current plan, allowing for adjustments in real-time. This adaptive planning could leverage reinforcement learning techniques to improve decision-making under uncertainty. Additionally, utilizing a belief state representation can help manage uncertainty in partially observable environments. By maintaining a probabilistic model of the environment, LaMMA-P can make informed decisions based on the likelihood of various states, rather than relying solely on deterministic assumptions. This approach would enhance the robustness of task execution, ensuring that robots can effectively collaborate even when they lack complete information about their environment.

What are the potential limitations of the PDDL-based approach, and how can LaMMA-P be further improved to address them?

The PDDL-based approach, while powerful for structured planning, has several limitations. One significant drawback is its reliance on a fully observable and static environment, which may not reflect real-world scenarios where conditions can change unpredictably. This limitation can lead to suboptimal plans if the environment evolves after the planning phase. To address these limitations, LaMMA-P can be improved by integrating more flexible planning paradigms, such as hierarchical task networks (HTNs) or temporal planning, which can accommodate changes in the environment and allow for more complex task structures. Additionally, enhancing the PDDL framework to support conditional planning and contingencies would enable the system to generate plans that can adapt to unforeseen circumstances. Another area for improvement is the scalability of the PDDL representation. As task complexity increases, the size of the PDDL problem can grow exponentially, leading to longer planning times. Implementing more efficient heuristics or utilizing machine learning techniques to predict and prioritize actions could mitigate this issue, improving the overall efficiency of the planning process.

What other applications beyond household tasks could benefit from the integration of language models and traditional planning techniques?

The integration of language models and traditional planning techniques, as exemplified by LaMMA-P, has broad applicability beyond household tasks. One significant area is in autonomous vehicles, where the ability to interpret natural language commands and translate them into actionable plans can enhance navigation and decision-making in complex traffic scenarios. Another promising application is in healthcare, particularly in robotic surgery or patient care. Robots equipped with language understanding can follow complex medical instructions, adapt to real-time changes in patient conditions, and coordinate with human medical staff, improving the efficiency and safety of healthcare delivery. In industrial automation, the combination of language models and planning techniques can optimize workflows in manufacturing settings. Robots can interpret high-level production goals and dynamically allocate tasks among themselves based on real-time conditions, enhancing productivity and reducing downtime. Finally, disaster response scenarios, such as search and rescue operations, can greatly benefit from this integration. Robots can receive vague or high-level instructions from human operators and autonomously plan and execute complex tasks in unpredictable environments, improving response times and effectiveness in critical situations.
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