The last two decades have seen a growing interest in the development of automated agents that are able to negotiate on the user’s behalf. When representing a user in a negotiation, it is essential for the agent to understand the user’s preferences, without exposing them to elicitation fatigue. To this end, we propose a new model in which a negotiating agent may incrementally elicit the user’s p...

Groups engaged in a mutual activity often need assistance in order to reach a joint decision. However, the group members’ personal preferences are often unknown and need to be collected. Querying for preferences can annoy the users. We suggest employing a voting mechanism that finds a winning item under incomplete settings. We present a practical method for eliciting the preferences, so that wi...

Preference relations can provide a more realistic model of random phenomena than quantita­ tive probability or belief functions. In order to use preference relations for reasoning under un­ certainty, it is necessary to perform sequential and parallel combinations of propagated infor­ mation in a qualitative inference network. This paper discusses the rules for such sequential and parallel comb...

The increasing complexity of products and services being offered by businesses has made providing customers with easy access to technical assistance an important business function. Therefore, most businesses operate call centers to respond to product related queries from consumers. An emerging model is to let a third-party to run the contact center for a business. Preference elicitation the pro...

In this paper, we describe area of recommender systems, with focus on user preference learning problem. We describe such system and identify some interesting problems. We will compare how well different approaches cope with some of the problems. This paper may serve as an introduction to the area of user preference learning with a hint on some interesting problems that have not been solved yet.

In this work we present a construction method for interval-valued fuzzy preference relations from a fuzzy preference relation and the representation of the lack of knowledge or ignorance that experts suffer when they define the membership values of the elements of that fuzzy preference relation. We also prove that, with this construction method, we obtain membership intervals for an element whi...

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We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent’s preferences, policy and optionally, the obtained reward sequence, from observations. We examine the relati...

We test whether induced mood states have an effect on elicited risk and time preferences in a conventional laboratory experiment. We jointly estimate risk and time preferences and find that subjects induced into a negative mood exhibit economically significant higher risk aversion than those in the control treatment. Those in the positive mood treatment exhibit even higher risk aversion. We fin...

In this paper, we define the concept of incomplete hesitant fuzzy preference relations to deal with the cases where the decision makers express their judgments by using hesitant fuzzy preference relations with incomplete information, and investigate the consistency of the incomplete hesitant fuzzypreference relations and obtain the reliable priority weights. We first establish a goal programmin...