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A Review of Explanation Methods for Bayesian Networks
 Knowledge Engineering Review
, 2000
"... One of the key factors for the acceptance of expert systems in real world domains is the capability to explain their reasoning. This paper describes the basic properties that characterize explanation methods and reviews the methods developed up to date for explanation in Bayesian networks. ..."
Abstract

Cited by 25 (3 self)
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One of the key factors for the acceptance of expert systems in real world domains is the capability to explain their reasoning. This paper describes the basic properties that characterize explanation methods and reviews the methods developed up to date for explanation in Bayesian networks.
Graduation Committee:
, 2006
"... Inference in Bayesian networks is used to calculate the posterior probability distributions of unobserved variables in a network. These posterior probability distributions are used to draw conclusions and are the basis for decisions, in the domain of a particular model. Inference is a complex proces ..."
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Inference in Bayesian networks is used to calculate the posterior probability distributions of unobserved variables in a network. These posterior probability distributions are used to draw conclusions and are the basis for decisions, in the domain of a particular model. Inference is a complex process and can be difficult to understand for even the most experienced Bayesian network users. In this thesis, we propose a technique to visualize important aspects of a Bayesian network, in order to make the process of inference more insightful. This technique consists of augmenting the visual representation of a Bayesian network with extra information. The only function of arcs in a Bayesian network is to indicate the relationships among the variables. We have used the arcs in a Bayesian network to show additional information: (1) the thickness of an arc is automatically adjusted to represent the strength of influence between two directly connected nodes and (2) the color of an arc is automatically adjusted to indicate the sign of influence between two directly connected nodes. Our technique does this in a novel, dynamic way, which is contextspecific and takes into account any indirect influences. We have implemented our technique and integrated it into a software package called GeNIe, which can be used for developing Bayesian networks and is developed at the Decision Systems Laboratory of the University of Pittsburgh. A qualitative empirical evaluation showed that our technique and implementation are easy to use and understand and give a user more insight into a particular Bayesian network. iii
Representing Coordination Relationships with Influence Diagrams
, 2001
"... It is well know the necessity of managing relationships among agents in a multiagent system to achieve coordinated behavior. One approach to manage such relationships consists of using an explicit representation of them, allowing each agent to choose its actions based on them. Previous work in t ..."
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It is well know the necessity of managing relationships among agents in a multiagent system to achieve coordinated behavior. One approach to manage such relationships consists of using an explicit representation of them, allowing each agent to choose its actions based on them. Previous work in the area have considered ideal situations, such as fully known environments, static relationships and shared mental states. In this paper we propose to represent relationships among agents and entities in a multiagent system by using influence diagrams.
REASONING By
, 2003
"... The field of medical informatics comprises many subdisciplines, united by a common interest in the establishment of standards to facilitate the sharing, reuse, and understanding of information. This work depends in large part on the ability of controlled medical terminologies to represent relevant c ..."
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The field of medical informatics comprises many subdisciplines, united by a common interest in the establishment of standards to facilitate the sharing, reuse, and understanding of information. This work depends in large part on the ability of controlled medical terminologies to represent relevant concepts. This work augments a controlled terminology to provide not only standardized content, but also standardized explanatory knowledge for use in expert systems. This experiment consisted of four phases centered on the use of the controlled terminology Systemized Nomenclature of Medicine (SNOMED). The first phase evaluated SNOMED’s ability to express explanatory knowledge for clinical pathology. The second developed the Normalized Medical Explanation (NORMEX) syntax for expressing and storing pathways of causal reasoning in the domain of clinical pathology. The third segment examined SNOMED’s capacity to represent concepts used in the NORMEX model of clinical pathology. The final phase incorporated NORMEXbased pathways of influence in a Bayesian network to assess ability to predict causal mechanisms as implied by serum analyte results.