Seminario "Bayesian networks without machine learning"


Room “Sala Seminari” - Abacus Building (U14)


Bayesian networks without machine learning



Prof. Peter J.F. Lucas

Professor Emeritus
University of Twente



With the by newspapers promised unemployment of computer scientists in sight, due to the success of GenAI, it is time to go back to the heavenly times when knowledge mattered more than data. At the beginning of AI and in particular the field of Bayesian networks and related probabilistic graphical models and logics, it appeared to be possible to develop working models without any data, purely based on domain  and domain-expert knowledge. In this talk I go through a couple of my research projects which exactly did that in the hope of convincing you that exploiting data has its advantages, but also its limitations, and that knowledge without data  is better than ignorance with data.

Short bio

Peter Lucas, MD PhD, is professor emeritus of Artificial Intelligence at the University of Twente. Previously he has been working at various other universities. He has been educated as a medical doctor as well as a computer scientist, and has been involved in research in the area of Artificial Intelligence, in particular knowledge-based systems and Bayesian networks, since the beginning of the 1980s. He has contributed to this area by theoretical as well as applied research. At the moment, his research interests include topics such as knowledge representation, decision-support systems, model-based diagnosis, Bayesian networks and statistical machine learning. He is currently involved in a number of multidisciplinary projects aiming to deliver medical decision support systems to oncologists and patients. He has extensively published in AI, computing science and medical informatics journals and conferences with more than 200 papers, wrote and edited a number of AI books, organised a number of workshops in the field, and edited several thematic issues of journals on the topics mentioned above.


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