Subjective Logic and Arguing with Evidence

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Online Reputation Systems for the Health Sector

People who are seeking medical advice and care often find it difficult to obtain reliable information about the quality and competence of health service providers. While transparent quality evaluation of products and services is commonplace in most commercial services, public access to information about the quality of health services is usually very restricted. Online reputation and rating systems represent an emerging trend in decision support for service consumers. Reputation systems are based on collecting information about other parties in order to derive measures of their trustworthiness or reliability on various aspects. More specifically these systems use the Internet for the collection of ratings and for dissemination of derived reputation scores. Online rating systems applied to the health sector are already emerging. This article describes robust principles for implementing online reputation systems in the health sector. In order to prevent uncontrolled ratings, our method ensures that only genuine consumers of a specific service can rate that service. The advantage of using online reputation systems in the health sector is that it can assist consumers when deciding which health services to use, and that it gives an incentive for high quality health services among health service providers

Rule mining on extended knowledge graphs

Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Predictive Reasoning in Subjective Bayesian Networks

Subjective Bayesian networks extend Bayesian networks by substituting the conditional probability distributions with subjective opinions. In that way they enable explicit representation of the uncertainty in the probabilistic information encoded in the network. In this paper we focus on predictive reasoning in subjective Bayesian networks and propose an inference method that is based on the operations of deduction and multiplication of subjective opinions. We demonstrate modelling and inference with subjective Bayesian networks through an example.

Machine Learning for Offensive Cyber Operations

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