How to measure agent responsibility in planning?
Measuring Responsibility in Multi-Agent Systems
This paper introduces methods to measure how responsible an individual agent is for a particular outcome in a multi-agent system. It moves beyond simply saying "yes" or "no" an agent is responsible and provides a degree of responsibility, making it more nuanced. It uses three ways to measure this: by counting agent behaviors, by calculating probabilities, and using information theory (entropy).
For LLM-based multi-agent systems, the key contribution is the idea of quantifying responsibility. This is important because in complex systems with multiple LLMs interacting, it can be hard to pinpoint which LLM contributed most to a specific result or error. The proposed metrics offer a way to measure these contributions, potentially paving the way for better debugging, control, and understanding of LLM-based multi-agent behavior. The integration with Alternating-time Temporal Logic (ATL) provides a formal framework to reason about and verify properties of these systems over time, considering the strategies and interactions of the different LLMs involved.