Recognizing the preferences of Decision Makers (DM) is crucial for multi-criteria decision aiding. We present a constructive preference learning methodology, called /robust ordinal regression/. This methodology links Operational Research with Artificial Intelligence, and as such, it confirms the current trend in mutual relations between OR and AI.
The lecture starts from an observation that the dominance relation established in the set of alternatives evaluated on multiple attributes (criteria, or voters, or states of the nature) is the only objective information that stems from the formulation of a multiple attribute decision problem (ordinal classification, or ranking, or choice - with multiobjective optimization being a particular case). While it permits to eliminate many irrelevant (i.e., dominated) alternatives, it leaves many alternatives incomparable. This situation may be addressed by taking into account preferences of the DM. Therefore, decision aiding methods require some preference information exhibiting a value system of a single or multiple DMs. The preference information has often the form of decision examples. It is used by robust ordinal regression to build a preference model, which is then applied on a non-dominated set of alternatives to arrive at a recommendation presented to the DM(s). Discovering preferences from data consisting of decision examples is a paradigm of learning from examples, well-known in AI. In practical decision aiding, the process composed of preference elicitation, preference modeling, and DM’s analysis of a recommendation, loops until the DM (or a group of DMs) accepts the recommendation or decides to change the problem setting. Such an interactive process is called constructive preference learning.
About the speaker
Roman Słowiński is a Professor and Founding Chair of the Laboratory of Intelligent Decision Support Systems at Poznań University of Technology, and a Professor in the Systems Research Institute of the Polish Academy of Sciences. As a full member of the Polish Academy of Sciences he has been its Vice-President in 2019–2022. He is also a member of Academia Europaea and Fellow of IEEE, IRSS, INFORMS, IFIP, IFORS, AAIA, and IAITQM. In his research, he combines Operational Research and Artificial Intelligence for Decision Aiding. Recipient of the EURO Gold Medal by the European Association of Operational Research Societies, Officer of Academic Palms of France. Doctor Honoris Causa of Polytechnic Faculty of Mons, University Paris Dauphine, Technical University of Crete, and Hellenic Mediterranean University. Honorary Professor of the Nanjing University of Aeronautics & Astronautics. Laureate of the 2005 Prize of the Foundation for Polish Science, and the Humboldt Research Award 2023. Since 1999, he is the co-ordinating editor-in-chief of the European Journal of Operational Research (Elsevier).
Prof. Słowiński's Google Scholar page
Technical sessions proposal submission: November 14, 2022 Paper submission (no extensions): May 23, 2023 Position paper submission: June 7, 2023
- Author notification: July 11, 2023
- Final paper submission, registration: July 31, 2023
- Discounted payment: August 15, 2023
- Conference date: September 17–20, 2023