MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction
The advent of large language model (LLM) powered AI agents has transformed the landscape of autonomous problem-solving. However, these AI agents often falter when faced with intricate, multi-step real-world tasks that require domain-specific procedural knowledge. A promising solution lies in the concept of reusable agent skills, which encapsulate effective problem-solving strategies, thus allowing agents to leverage prior experiences. Despite their potential, the curation of such skills has predominantly remained a manual process, necessitating human experts to distill complex domain knowledge into actionable guidelines.
In response to this challenge, a new framework known as Multi-agent INduction and Deduction for Skills (MIND-Skill) has been introduced. This innovative system automatically induces generalizable skills from successful trajectories while ensuring robust quality guarantees. MIND-Skill comprises two principal components: an induction agent responsible for abstracting reusable skills from successful trajectories, and a deduction agent that reconstructs trajectories by adhering to the induced skills.
Framework Components
The dual-agent architecture of MIND-Skill leverages both induction and deduction processes to generate high-quality skills. Below are the key components:
- Induction Agent: This agent is tasked with the abstraction of reusable skills from successful trajectories. By analyzing past successes, it identifies patterns and strategies that can be generalized for future problem-solving.
- Deduction Agent: In contrast, the deduction agent reconstructs trajectories by implementing the skills identified by the induction agent. It tests the validity and effectiveness of these skills in real-world scenarios.
Quality Assurance Mechanisms
To ensure the quality of the generated skills, MIND-Skill employs several innovative loss functions:
- Reconstruction Loss: This component compares the original input trajectories with the reconstructed ones to assess fidelity and accuracy.
- Outcome Loss: This metric enforces the correctness of the reconstructed trajectories, ensuring that the end results align with expected outcomes.
- Rubric Loss: Focused on documentation quality, this loss regularizes the abstraction level of the generated skills according to predefined criteria, thus maintaining high standards in skill generation.
These textual losses are optimized simultaneously using the innovative TextGrad method, which aids in refining the skill generation process. The resulting skills are then evaluated against held-out tasks that were not part of the optimization process, providing an unbiased measure of their effectiveness.
Empirical Results
Extensive experiments conducted on benchmark platforms such as AppWorld and BFCL-v3 have demonstrated that MIND-Skill consistently outperforms existing skill generation methods. The results affirm the framework’s capability to produce high-quality, reusable skills that significantly enhance the performance of AI agents in complex problem-solving scenarios.
In summary, MIND-Skill represents a significant advancement in the automation of skill generation for AI agents. By integrating multi-agent induction and deduction processes with robust quality assurance mechanisms, this framework not only streamlines the creation of reusable skills but also ensures their reliability and effectiveness in real-world applications.
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