Please use this identifier to cite or link to this item: https://doi.org/10.1080/08839510802379600
Title: Multi-agent graphical decision models in medicine
Authors: Zeng, Y.
Poh, K.-L. 
Issue Date: Jan-2009
Source: Zeng, Y., Poh, K.-L. (2009-01). Multi-agent graphical decision models in medicine. Applied Artificial Intelligence 23 (1) : 103-122. ScholarBank@NUS Repository. https://doi.org/10.1080/08839510802379600
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.
Source Title: Applied Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/63190
ISSN: 08839514
DOI: 10.1080/08839510802379600
Appears in Collections:Staff Publications

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