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Talks & Seminars
Title: Bayesian Argumentation via Delphi (BARD)
Prof. Ann Nicholson, Monash University
Date & Time: March 22, 2017 15:00
Venue: F. C. Kohli Auditorium, B Block, 01st Floor, Department of Computer Science and Engineering, Kanwal Rekhi (KReSIT) Building
Abstract:
Many areas of human endeavour require decision making about complex systems, where there is: uncertainty about the causal process driving the system; limited and possibly inaccurate information about the current state of the system; and uncertainty about the effects of actions or interventions. Bayesian networks have become a state-of-the-art technology to support decision-making under uncertainty. These models can combine data, evidence, opinion and guesstimates to help decisions makers combine probabilities and take into account costs and benefits. In this seminar, I will describe the BARD project, funded by IARPA through the CREATE program, to design and build an online tool to support Delphi-style group construction of Augmented Causal Bayesian Networks and the production of structured and transparent analysis reports.
Speaker Profile:
Professor Ann Nicholson is the Deputy Dean in the Faculty of Information Technology at Monash University. After completing her BSc (Hons) and MSc in Computer Science at the University of Melbourne, in 1988 she was awarded a Rhodes scholarship to Oxford, where she did her doctorate in the Robotics Research Group. After completing a post-doc at Brown University, she returned to Australia to take up a lecturing position at Monash in 1994. Prof. Nicholson researches in the broad areas of Artificial Intelligence and machine learning. She is a leading international researcher in the specialised area of Bayesian networks, now the dominant technology for probabilistic causal modeling in intelligent systems. She has applied Bayesian Network technology to problem-solving in many domains including meteorology, epidemiology, medicine, education and environmental science. Examples include the use of BNs in biosecurity risk assessment, predicting the impact of conservation actions on threats and habitats of threatened species, fog forecasting and decision support for clinical cardiovascular risk assessment.
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