Π5 – Τεχνική έκθεση (βιβλιογραφική ανασκόπηση συνδυασμού μεθόδων τεχνητής νοημοσύνης και πολυκριτήριας ανάλυσης)

Η παρούσα βιβλιογραφική ανασκόπηση επικεντρώνεται στην ανάλυση της σχέσης ανάμεσα σε τεχνικές ΠΑΑ και μεθοδολογίες από το πεδίο της ΤΝ, καθώς και του τρόπου με τον οποίο η ευστάθεια αντιμετωπίζεται στα δύο πεδία. Η καταγραφή αυτή συμβάλει στον εντοπισμό συνεργειών που μπορούν να προκύψουν από την ανάπτυξη διαδικασιών που συνδυάζουν ιδέες, έννοιες και αρχές από τα πεδία της ΠΑΑ και της ΤΝ για την καλύτερη μελέτη της ευστάθειας σε προβλήματα λήψης αποφάσεων. H βιβλιογραφική ανασκόπηση πραγματοποιείται στα πλαίσια διαδικασιών ανάπτυξης μοντέλων αποφάσεων μέσω της αναλυτικής-συνθετικής προσέγγισης (preference disaggregation approach, Jacquet-Lagrèze & Siskos, 2001) της ΠΑΑ, η οποία όπως θα αναλυθεί έχει σημαντικά κοινά στοιχεία με τεχνικές από το χώρο της ΤΝ και ιδιαίτερα με μεθοδολογίες μηχανικής μάθησης (machine learning)

Dimensionality reduction for multi-criteria problems: an application to the decommissioning of oil and gas installations

This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the training set. Computational experiments illustrate the effectiveness of the proposed approach

Kernel-based learning methods for preference aggregation

The mathematical representation of human preferences has been a subject of study for researchers in different fields. In multi-criteria decision making (MCDM) and fuzzy modeling, preference models are typically constructed by interacting with the human decision maker (DM). However, it is known that a DM often has difficulties to specify precise values for certain parameters of the model. He/she instead feels more comfortable to give holistic judgements for some of the alternatives. Inference and elicitation procedures then assist the DM to find a satisfactory model and to assess unjudged alternatives. In a related but more statistical way, machine learning algorithms can also infer preference models with similar setups and purposes, but here less interaction with the DM is required/allowed. In this article we discuss the main differences between both types of inference and, in particular, we present a hybrid approach that combines the best of both worlds. This approach consists of a very general kernel-based framework for constructing and inferring preference models. Additive models, for which interpretability is preserved, and utility models can be considered as special cases. Besides generality, important benefits of this approach are its robustness to noise and good scalability. We show in detail how this framework can be utilized to aggregate single-criterion outranking relations, resulting in a flexible class of preference models for which domain knowledge can be specified by a DM