Agent-Oriented Planning in Multi-Agent Systems (2410.02189v2)

Abstract: Through the collaboration of multiple LLM-empowered agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems. Given the user queries, the meta-agents, serving as the brain within multi-agent systems, are required to decompose the queries into multiple sub-tasks that can be allocated to suitable agents capable of solving them, so-called agent-oriented planning. In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy, to ensure that each sub-task can be effectively resolved, resulting in satisfactory responses to user queries. These principles further inspire us to propose AOP, a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process followed by an effective and efficient evaluation via a reward model. According to the evaluation results, the meta-agent is also responsible for promptly making necessary adjustments to sub-tasks and scheduling. Besides, we integrate a feedback loop into AOP to further enhance the effectiveness and robustness of such a problem-solving process. Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems. The source code is available at https://github.com/lalaliat/Agent-Oriented-Planning

• The paper introduces a novel meta-agent framework that decomposes complex tasks among agents based on key principles of solvability, completeness, and non-redundancy.
• It employs a fast task decomposition and allocation process using a reward model to evaluate sub-task success without direct execution.
• Experimental results demonstrate significant gains in accuracy and reliability over traditional single-agent and multi-agent strategies.

Agent-Oriented Planning in Multi-Agent Systems

The paper "Agent-Oriented Planning in Multi-Agent Systems" proposes a novel framework designed to enhance agent-oriented planning within multi-agent systems, emphasizing the collaboration of multiple agents to solve complex real-world problems. Central to the framework is the concept of the meta-agent, which acts as a controller or planner responsible for task decomposition and allocation among diverse agents. The paper identifies three crucial design principles—solvability, completeness, and non-redundancy—necessary to ensure that sub-tasks are effectively resolved and that the original queries are addressed comprehensively.

The authors suggest a systematic framework featuring a fast task decomposition and allocation process, complemented by a reward model for efficient evaluation. The meta-agent's responsibilities extend to evaluating the performance of the agents and adjusting task assignments as needed. Additionally, the framework incorporates a feedback loop aimed at enhancing the robustness and effectiveness of the problem-solving process.

Key highlights of the paper include:

1. Design Principles: The proposed framework is guided by three principles:

• Solvability: Ensures each sub-task is independently solvable by a single agent, allowing for reliable responses.
• Completeness: The set of sub-tasks must cover all necessary elements of the original query to provide a comprehensive solution.
• Non-redundancy: Ensures no overlap or superfluous sub-tasks exist, enhancing efficiency and relevance.

1. Fast Decomposition and Allocation: The framework relies on the meta-agent's ability to quickly analyze and distribute tasks to the most suitable agents. This process is designed to be efficient, leveraging the powerful capabilities of LLMs without manually invoking agent operations.
2. Evaluation via Reward Model: A reward model is introduced to evaluate the potential success of sub-task allocations without executing them. This model predicts task completion outcomes, facilitating strategic reassignment if initial allocations fall short.
3. Sub-Task Adjustment Mechanisms: The framework identifies when sub-tasks may be inadequately defined or too complex, suggesting replanning, detailed planning, or re-description as necessary. This flexibility ensures alignment with agent capabilities and problem requirements.
4. Feedback and Improvement: An automatic feedback loop involving representative works allows ongoing updates and refinements to sub-task allocations, promoting continuous learning and optimization across multi-agent interactions.

The experimental results demonstrate the framework's effectiveness, showcasing significant advancements over both single-agent systems and existing multi-agent strategies. Extensive testing on a reasoning dataset involving multiple agents confirms its potential for practical applications, achieving higher accuracy and reliability compared to traditional methods.

The implications of this research are substantial for future developments in AI and multi-agent systems. By adhering to the identified principles, systems can achieve greater stability and efficiency. Moreover, such frameworks could be applied in diverse fields, ranging from automated software development to complex decision-making environments, effectively bridging the gap between artificial intelligence and real-world applications. Continuing to enhance reward model precision and integrating finely tuned feedback mechanisms may further boost system performance and reliability.