Plenary Talks
Constructive Preference Learning for Multiple Attribute Decision Aiding
Roman Słowiński(Poznań University of Technology, and Polish Academy of Sciences, Poland)
Identification of Decision Maker’s (DM’s) preferences is crucial for multiple attribute decision aiding. We present a constructive preference learning methodology, called Robust Ordinal Regression, for Multiple Criteria Decision Aiding, for Group Decision, and for Decision under Uncertainty. This methodology links Operational Research with Artificial Intelligence, and as such, it confirms the current trend in mutual relations between OR and AI. It is known 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 comes 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 does not compare completely all of them, resulting in a situation where many alternatives remain incomparable. This situation may be addressed by taking into account preferences of the DM or a group of DMs. Therefore, decision aiding methods require some preference information elicited from a DM or from a group of DMs. This information is used to build more or less explicit preference model, which is then applied on a non-dominated set of alternatives to arrive at a recommendation presented to the DM(s). 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.
On the Observability of Smart Grids and Related Optimization Methods
Claudia D'Ambrosio
(CNRS affiliated at LIX, École Polytechnique, France )
Abstract follows