Conferencia de Ann Nicholson (Universidad de Melbourne)

Lunes, 7 de Junio de 17:30 a 18:30 horas en el Salón de Actos del  I3A.  

Title:  Incorporating expert knowledge when learning bayesian network structure: Heart failure as a case study

Abstract: Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence (AI), with medicine one of its most popular application area.  Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remains underexplored. In this seminar, I will present a case study of this combination using public-domain data for heart failure. We run an automated causal discovery system (CaMML), which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results.  These techniques are presented within a wider context of knowledge engineering with Bayesian networks (KEBN).

Bio: Ann Nicholson is visiting Warwick until the end of June.  She is on sabbatical from the Clayton School of IT at Monash University, where she is an Associate Professor.  She received her B.Sc (Hons) and M.Sc. degrees in Computer Science from the University of Melbourne. In 1992 she received her Ph.D. in Engineering from the University of Oxford, where she applied dynamic Bayesian networks to robot monitoring. After 2 years as a post-doctoral research fellow in Computer Science at Brown University, she took up a position at Monash University in 1994. Her areas of research interest are reasoning under uncertainty, Bayesian networks, knowledge engineering, decision support, user modelling, stochastic planning and monitoring, artificial life and evolutionary ethics.