Abstract:

Reasoning with preferences has gained increasing attention in the field of artificial intelligence (AI) in the recent years. Amongst others, this interest has led to the emergence of preference learning as a new branch of machine learning. Roughly speaking, preference learning refers to the development and analysis of methods for learning preference models from explicit or implicit preference information, that is, models useful for explaining revealed and predicting future preferences. Starting with a brief introduction to preferences in AI in general and machine learning in particular, this talk will focus on methods and algorithms for ranking problems. These are (learning) problems in which preferences are represented in terms of a total order over an underlying set of choice alternatives, where both the order relation and the choice set can be contextualized. Based on different types of preference feedback, the goal of the learner is to induce a model that allows for predicting such order relations in an accurate manner.