BOIL: Learning Environment Personalized Information

2026-04-18

AI Summary

This work addresses the challenge faced by multi-agent systems in complex environments, where extracting critical information from limited observations to guide long-term behavior remains difficult. To this end, the paper proposes the Black-box Oracle Information Learning (BOIL) framework, which uniquely integrates the PageRank algorithm with public information maximization to construct a scalable environment-structure-aware mechanism. This mechanism enables the extraction of personalized guidance signals and the modeling of multi-agent policy distributions without requiring prior environmental knowledge. Evaluated on tasks such as coverage, patrolling, and stochastic reachability, BOIL significantly outperforms heuristic baselines, yielding policies that demonstrate superior performance and robustness over extended time horizons.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Navigating complex environments poses challenges for multi-agent systems, requiring efficient extraction of insights from limited information. In this paper, we introduce the Blackbox Oracle Information Learning (BOIL) process, a scalable solution for extracting valuable insights from the environment structure. Leveraging the Pagerank algorithm and common information maximization, BOIL facilitates the extraction of information to guide long-term agent behavior applicable to problems such as coverage, patrolling, and stochastic reachability. Through experiments, we demonstrate the efficacy of BOIL in generating strategy distributions conducive to improved performance over extended time horizons, surpassing heuristic approaches in complex environments.

Problem

• multi-agent systems
• complex environments
• information extraction
• long-term behavior
• stochastic reachability

Innovation

• Blackbox Oracle Information Learning
• PageRank algorithm
• common information maximization
• multi-agent systems
• environment structure