What can we learn from experiments in multiobjective decision analysis?
IEEE Transactions on Systems, Man and Cybernetics
A comparison of interactive multiple-objective decision making procedures
Computers and Operations Research
Decision support systems: learning from visual interactive modelling
Proceedings of the conference on First specialized conference on decision support systems
The effects of anchoring in interactive MCDM solution methods
Computers and Operations Research
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Interactive Robust Multiobjective Optimization Driven by Decision Rule Preference Model
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Robust ordinal regression in preference learning and ranking
Machine Learning
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Learning is inherently connected with Interactive Multiobjective Optimization (IMO), therefore, a systematic analysis of IMO from the learning perspective is worthwhile. After an introduction to the nature and the interest of learning within IMO, we consider two complementary aspects of learning: individual learning, i.e., what the decision maker can learn, and model or machine learning, i.e., what the formal model can learn in the course of an IMO procedure. Finally, we discuss how one might investigate learning experimentally, in order to understand how to better support decision makers. Experiments involving a human decision maker or a virtual decision maker are considered.