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
Preference elicitation via theory refinement
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Utility estimation in large preference graphs using a search
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Expert Systems with Applications: An International Journal
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Before an autonomous agent can perform automated negotiation on behalf of a user in an electronic commerce transaction, the user's preferences over the set of outcomes must be learned as accurately as possible. This paper presents a structure, a Conditional Outcome Preference Network (COP-network), for modeling preferences directly elicited from a user. The COP-network then expands to indicate all preferences that can be inferred as a result. The network can be easily checked for consistency and redundancy, and can be used to determine quickly whether one outcome is preferred over another. An important feature of the COP-network is that conditional preferences, where a user's preference over outcomes depends on whether particular attribute values are included, can be modeled and inferred as well. If the agent also knows the user's utilities for some of the possible outcomes, then these can be considered in the COP-network as well. Three techniques for estimating utilities based on the specified preferences and utilities are described. One such technique, which works by first estimating utilities for long chains of outcomes for which preferences are known, is shown to be the most effective.