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The complexity of unsupervised learning of lexicographic preferences
This paper considers the task of learning users' preferences on a combinatorial set of alternatives, as generally used by online configurators, for example.
An extended Knowledge Compilation Map for Conditional Preference Statements-based and Generalized Additive Utilities-based Languages
Conditional preference statements have been used to compactly represent preferences over combinatorial domains.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
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