Monte carlo methods for preference learning

Authors:
Paolo Viappiani
Affiliations:
Department of Computer Science, Aalborg University, Denmark
Venue:
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Year:
2012

Citing 4
Cited 1

Making Rational Decisions Using Adaptive Utility Elicitation

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A POMDP formulation of preference elicitation problems

Eighteenth national conference on Artificial intelligence
Preference-based policy learning

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Preference-based policy iteration: leveraging preference learning for reinforcement learning

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I

APRIL: active preference learning-based reinforcement learning

ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II

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Abstract

Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query the users about their preferences and give recommendations based on the system's belief about the utility function. Critical to these applications is the acquisition of prior distribution about the utility parameters and the possibility of real time Bayesian inference. In this paper we consider Monte Carlo methods for these problems.