The Evolution of Operational Research and Decision Support under the influence of the Artificial Intelligence Paradigm (只有英文版本)

In this seminar, I will present a constructive preference learning methodology, called robust ordinal regression (ROR), for multiple criteria decision aiding. This methodology links Operational Research with Artificial intelligence, and as such, it confirms the current trend in mutual relations between these disciplines. To provide a ‘best compromise’ solution to a multiple criteria decision problem, decision aiding methods require some preference information exhibiting a value system of a single or multiple DMs. In ROR, the preference information has the form of decision examples. They may either be provided by the DM on a set of real or hypothetical alternatives or may come from observation of DM’s past decisions. This information is used 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). Additionally, we proposed an interactive process, called constructive preference learning, for practical decision aiding. We describe this process for three types of preference models: (i) utility functions, (ii) outranking relations, and (iii) sets of monotonic decision rules. The case of a hierarchical structure of the set of criteria will be discussed, and the transparency and explainability features required from preference learning will be discussed on the example of interactive multiobjective optimization.