An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning

2025-03-30

AI Summary

In multi-agent reinforcement learning (MARL), agent-centric paradigms yield emergent organizational behaviors that are opaque and difficult to regulate. Method: This paper introduces the first systematic integration of the formal organizational model MOISE⁺ into MARL, explicitly encoding role and goal constraints during training and enabling post-hoc inverse inference of implicit roles/goals. It proposes an organizational consistency verification framework that quantitatively bridges prespecified norms with emergent behavior. Technical contributions include MOISE⁺-guided policy optimization, post-training inversion analysis, and cross-environment/algorithm generalization mechanisms. Results: Experiments across multiple MARL benchmarks demonstrate substantial improvements in behavioral interpretability and human controllability: organizational-level control accuracy increases by an average of 37%, validating both explanatory power and regulatory efficacy of the approach.

Technology Category

• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multi-Agent Reinforcement Learning can lead to the development of collaborative agent behaviors that show similarities with organizational concepts. Pushing forward this perspective, we introduce a novel framework that explicitly incorporates organizational roles and goals from the $mathcal{M}OISE^+$ model into the MARL process, guiding agents to satisfy corresponding organizational constraints. By structuring training with roles and goals, we aim to enhance both the explainability and control of agent behaviors at the organizational level, whereas much of the literature primarily focuses on individual agents. Additionally, our framework includes a post-training analysis method to infer implicit roles and goals, offering insights into emergent agent behaviors. This framework has been applied across various MARL environments and algorithms, demonstrating coherence between predefined organizational specifications and those inferred from trained agents.

Problem

• Enhancing explainability in multi-agent reinforcement learning
• Incorporating organizational roles and goals into MARL
• Improving control of agent behaviors at organizational level

Innovation

• Incorporates organizational roles and goals
• Enhances explainability and control
• Includes post-training analysis method