Belief Propagation for Structured Decision Making

TL;DR

This paper introduces a variational framework based on belief propagation algorithms for structured cooperative decision-making in graphical models, addressing a gap in applying variational methods to influence diagrams and multi-agent decisions.

Contribution

It develops a general variational approach for decision-making, proposes new belief propagation-like algorithms, and provides theoretical and empirical analysis of these methods.

Findings

The algorithms effectively solve structured cooperative decision problems.

Theoretical analysis confirms convergence properties.

Empirical results demonstrate practical applicability.

Abstract

Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically.