Multi-agent graphical decision models in medicine

Abstract

Many practical applications in the medical domain require a cooperative decision with multiple entities (agents). These applications are instances of a multi-agent decision problem. This complex decision problem often concerns a large knowledge domain and involves some agency properties. It disables traditional methods on probabilistic graphical decision models. In this article, we propose a new representation including multiply sectioned influence diagrams (MSIDs) and hyper relevance graphs (HRGs). An MSID represents decision problems involving multiple agents in a distributed and flexible fashion, while an HRG encodes organizational relationships in a multi-agent system. Subsequently, a symbolic method is extended to facilitate the model verification with the aim of building a valid decision model. An evaluation algorithm based on the junction tree algorithm is developed to solve an MSID. Some relevant evaluation strategies are analyzed. The decision problem on the Severe Acute Respiratory Syndrome (SARS) control is illustrated with our proposed methodologies throughout this article.