We consider how to combine the preferences of multiple agents despite the presence of incompleteness and incomparability in their preference orderings. An agent’s preference ordering may be incomplete because, for example, there is an ongoing preference elicitation process. It may also contain incomparability as this is useful, for example, in multi-criteria scenarios. We focus on the problem of computing the possible and necessary winners, that is, those outcomes which can be or always are the most preferred for the agents. Possible and necessary winners are useful in many scenarios including preference elicitation. First we show that computing the sets of possible and necessary winners is in general a difficult problem as is providing a good approximation of such sets. Then we identify general properties of the preference aggregation function which are sufficient for such sets to be computed in polynomial time. Finally, we show how possible and necessary winners can be used to focus preference elicitation. 1

Abstract-We describe the representation of uncertain knowledge in a conference-scheduling system, which may include incomplete information about available resources, conference events, and scheduling constraints. We then explain the use of this incomplete knowledge in the evaluation of schedule quality. 1.

The quality of the results produced by personalized e-service applications like product recommenders, buying advisory applications, or product configurators is strongly determined by the accuracy of the system’s estimate of the individual customer’s real needs and preferences. In particular in domains where customers cannot be classified automatically, e.g., based on past buying behavior, these needs have to be interactively elicited by questioning the user. In many existing systems only a “one-style-fits-all ” approach based on static fill-out forms is chosen. However, this does not take the user’s background or capabilities into account, which consequently leads to a poor quality of the acquired user model. In this paper, we show how extensive personalization of the user preference elicitation process itself can significantly improve the accuracy of interactively acquired user models. A comprehensive view on adaptation and personalization opportunities in the elicitation process is developed and corresponding examples for the domain of interactive buying advisory are given. The presented personalization and adaptation techniques are implemented in a domain-independent software framework for building interactive advisory applications. We describe specific architectural requirements for such a system and discuss results from various real-world applications. 1.

Abstract-We consider the task of scheduling a conference based on incomplete data about available resource and scheduling constraints, and describe a procedure for automated elicitation of additional data. This procedure is part of an interactive system for scheduling under uncertainty, which identifies critical missing information, generates related questions to the human administrator, and uses answers to improve the schedule. I.

Abstract—We describe a scheduling system that supports collaboration between the user and automated optimizer. It enables the user to monitor the optimizer decisions, make any of the decisions manually, and leave the other decisions to the system. Furthermore, it identifies the tasks that require the user’s participation, and asks for assistance with these tasks. W

Today, result sets of skyline queries are unmanageable due to their exponential growth with the number of query predicates. In this paper we discuss the incremental re-computation of skylines based on additional information elicited from the user. Extending the traditional case of totally ordered domains, we consider preferences in their most general form as strict partial orders of attribute values. After getting an initial skyline set our basic approach aims at interactively increasing the system’s information about the user’s wishes explicitly including indifferences. The additional knowledge then is incorporated into the preference information and constantly reduces skyline sizes. In fact, our approach even allows users to specify trade-offs between different query predicates, thus effectively decreasing the query dimensionality. We give theoretical proof for the soundness and consistence of the extended preference information and an extensive experimental evaluation of the efficiency of our approach. On average, skyline sizes can be considerably decreased in each elicitation step.

Service-oriented computing provides the right means for building flexible systems that allow dynamic configuration and on-the-fly composition. In order to realize this vision, the system must be able to choose the most suitable service from a large and constantly changing number of providers. We present an approach for selecting services based on a coherent conceptual policy model and a service utility measure. Our framework is capable of capturing technical and application specific aspects of services and incorporates them into the decision making process. We assist the user in establishing the utility measure from existing policies by attaching value function patterns to the individual attributes. Drawing from the areas of utility theory, foundational ontology, and electronic markets, our work is a promising approach for unifying the heterogeneous methodologies in the service selection process. 1

Preference-based queries often referred to as skyline queries play an important role in cooperative query processing. However, their prohibitive result sizes pose a severe challenge to the paradigm‟s practical applicability. In this paper we discuss the incremental re-computation of skylines based on additional information elicited from the user. Extending the traditional case of totally ordered domains, we consider preferences in their most general form as strict partial orders of attribute values. After getting an initial skyline set our approach aims at incrementally increasing the system‟s information about the user‟s wishes. This additional knowledge then is incorporated into the preference information and constantly reduces skyline sizes. In particular, our approach also allows users to specify trade-offs between different query attributes, thus effectively decreasing the query dimensionality. We provide the required theoretical foundations for modeling preferences and equivalences, show how to compute incremented skylines, and proof the correctness of the algorithm. Moreover, we show that incremented skyline computation can take advantage of locality and database indices and thus the performance of the algorithm can be additionally increased.

Negotiation is a complex emotional decision-making process aiming to reach an agreement to exchange goods or services. Although a daily activity, few people are effective negotiators. Existing support systems make a significant improvement if the negotiation space is well-understood, because computers can better cope with the computational complexity. However, the negotiation space can only be properly developed if the human parties jointly explore their interests. The inherent semantic problem and the emotional issues involved make that negotiation cannot be handled by artificial intelligence alone, and a human-machine collaborative system is required. This interest paper presents research goals, ideas, challenges and an approach towards creating the next generation of negotiation support systems.

While trying to satisfy a user’s preferences for a resource, partially stated or completely unknown preferences can be very disrupting. Unfortunately most of the time a user will not specify her preferences perfectly, and her partially stated or unknown preferences will be so numerous that she will not be willing to provide clarifications for all. To complicate things further we may not have perfect information about the resource as well and while we can ask an information source about the resource, the problem of “too many potential questions ” remains if we were to expect getting answers for all the imperfectly specified properties. In cases like these, we need a mechanism for figuring out which questions would yield a bigger improvement in allocating resources to users so that we can ask as few questions as it is possible to answer and get as high of a improvement as possible An example of such a problem is optimization for calculating the best assignment of a set of rooms to a set of sessions. The assignment depends on the various properties of rooms as well as the requirements for each session and the properties of these