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—This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that a...
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or ...
Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query users about their preferences and ...
We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous w...
Combinatorial auctions have been used in procurement markets with economies of scope. Preference elicitation is already a problem in single-unit combinatorial auctions, but it bec...
Martin Bichler, Stefan Schneider, Kemal Guler, Meh...
Abstract: Preference elicitation is often used in e-services to create product recommendations for their customers. We present an approach for applying preference elicitation techn...
Preference elicitation is a central problem in AI, and has received significant attention in single-agent settings. It is also a key problem in multiagent systems, but has receive...
We consider auction design in a setting with costly preference elicitation. Well designed auctions can help to avoid unnecessary elicitation while determining efficient allocations...
We consider soft constraint problems where some of the preferences may be unspecified. This models, for example, settings where agents are distributed and have privacy issues, or ...
Mirco Gelain, Maria Silvia Pini, Francesca Rossi, ...
Minimax-regret preference elicitation allows intelligent decisions to be made on behalf of people facing risky choices. Standard gamble queries, a vital tool in this type of prefe...