This recollection of John Riedl, founding coeditor-in-chief of the ACM Transactions on Interactive Intelligent Systems , presents a picture by editors of the journal of what it was like to collaborate and interact with him.

Intelligent technologies have been used in various ways to support more effective representation and processing of media and documents in terms of the events that they refer to. This demo presents some innovations that have been introduced in a web-based interface to a repository of media and documents that are organized in terms of hierarchically structured events.

This editorial introduction explains the motivation and origin of the TiiS special issue on Highlights of the Decade in Interactive Intelligent Systems and shows how its five articles exemplify the types of research contribution that TiiS aims to encourage and publish.

There are many reasons why a researcher in the area of intelligent user interfaces (IUI) might decide not to attend this tutorial: 1. I didn't take a tutorial at my last conference. 2. I'm not the kind of person who takes tutorials at conferences. 3. Hardly any of my friends take tutorials. 4. It's not the policy of my organization for researchers to take tutorials. 5. The topic does not seem at first glance to be one that I need to pay attention to. 6. The cost of taking this tutorial is immediate and clear, but the benefits would be spread out over years, and I'm not sure what they would be, or how large. 7. I'm feeling impatient, and I want to move on to the next page. But here's the catch: Every one of the reasons listed above can also cause users to disregard the recommendations of the researcher's novel recommender system, reject the adaptations of their adaptive interface, or generally make choices concerning the use of IUI innovations that seem wrong to the researchers who developed them and maybe to the users, if they took the trouble to reconsider them. In other words: IUI researchers do need to know something about human choice and decision making. Just consulting common knowledge or reading a textbook isn't enough, since the most IUI relevant concepts and results are found in a number of subareas of research on judgment and decision making, learning and habitual behavior, and strategies for influencing choices; and their relevance to IUI issues is hardly ever made explicit. This new tutorial aims to fill this gap: It presents relevant concepts and insights from psychological research with explicit reference to issues that IUI researchers typically encounter. With the help of the take-home supplementary material, participants will be able to understand and predict more realistically (though less confidently) the choices that users make about or with the help of their systems. And they can try in more imaginative ways to influence these choices. They might also think of a fresh research issue for their next project proposal.

This editorial introduction describes the aims and scope of the ACM Transactions on Interactive Intelligent Systems , explains how it aims to constitute a landmark addition to the publication landscape, and shows how the five articles in this inaugural issue fit into the journal's conception.

The goal of this full-day workshop is to arrive at a synthesis of knowledge that will help people who work with intelligent user interfaces to predict and explain how users' attitudes and behavior toward aspects of such systems (a) differ from one user to the next and (b) change over time.

Selecting relevant people is crucial for collaborative systems exploiting other users' experiences. With the new developments of the Web and ubiquitous technologies, various user data may support people selection. Given the wide range of user data sources, the question is now how to select appropriate users meeting the information seeker's goal. We propose recommendations for the design of people search interfaces, providing an overview of the user data and tools of relevance and two examples of how such recommendations can be met in one single interface, ensuring the selection of appropriate and reachable people. We also show applications of people search interfaces in different scenarios.

This full-day tutorial is designed to convey an up-to-date, active understanding of a representative set of current developments in recommender systems that will help the participants to conduct cutting-edge research and/or to work more effectively with the currently widespread recommendation technology.

Proper usability evaluations of spoken dialogue systems are costly and cumbersome to carry out. In this paper, we present a new approach for facilitating usability evaluations which is based on user error simulations. The idea is to replace real users with simulations derived from empirical observations of users ’ erroneous behavior. The simulated errors must cover both system-driven errors (e.g., due to poor speech recognition) as well as conceptual errors and slips of the user, because neither alone is predictive of perceived usability. The simulation is integrated into a workbench which produces reports of typical and rare errors, and which allows usability ratings to be predicted. If successful, this workbench will help designers in making choices between system versions and lower testing costs at early phases of development. Challenges to the approach are discussed and solutions proposed. Index Terms: spoken-dialogue system, evaluation, usability 1.

Systems that recommend items to a group of two or more users raise a number of challenging issues that are so far only partly understood. This paper identifies four of these issues and points out that they have been dealt with to only a limited extent in the group recommender systems that have been developed so far. The issues are especially important in settings where group members specify their preferences explicitly and where they are not able to engage in face-to-face interaction. We illustrate some of the solutions discussed with reference to the TRAVEL DECISION FORUM prototype. The issues concern (a) the design of suitable preference elicitation and aggregation methods, in particular nonmanipulable aggregation mechanisms; and (b) ways of making members aware of each other's preferences and motivational orientations, such as the use of animated representatives of group members.

We present a group recommender system for vacations that helps group members who are not able to communicate synchronously to specify their preferences collaboratively and to arrive at an agreement about an overall solution. The system's design includes two innovations in visual user interfaces: 1. An interface for collaborative preference specification offers various ways in which one group member can view and perhaps copy the previously specified preferences of other users. This interface has been found to further mutual understanding and agreement. The same interface is used by the system to display recommended solutions and to visualize the extent to which a solution satisfies the preferences of the various group members. 2. In a novel application of animated characters, each character serves as a representative of a group member who is not currently available for communication. By responding with speech, facial expressions, and gesture to proposed solutions, a representative conveys to the current real user some key aspects of the corresponding real group member's responses to a proposed solution. Taken together, these two aspects of the interface provide complementary and partly redundant means by which a group member can achieve awareness of the preferences and responses of other group members: an abstract, complete, graphical representation and a concrete, selective, human-like representation. By allowing users to choose flexibly which representation they will attend to under what circumstances, we aim to move beyond the usual debates about the relative merits of these two general types of representation.

We extend Darwiche's differential approach to inference in Bayesian networks (BNs) to handle specific problems that arise in the context of dynamic Bayesian networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial. We then show how procedures for the computation of corresponding polynomials for DBNs can be derived. These procedures permit not only an exact roll-up of old time slices but also a constant-space evaluation of DBNs. The method is applicable to both forward and backward propagation, and it does not presuppose that each time slice of the DBN has the same structure. It is compatible with approximative methods for roll-up and evaluation of DBNs. Finally, we discuss further ways of improving efficiency, referring as an example to a mobile system in which the computation is distributed over a normal workstation and a resource-limited mobile device. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 727–748, 2004.

We are exploring a class of decision-theoretic handheld systems that give a user personalized advice about how to explore an indoor area in search of products or information. An initial user test in a simple mockup of a shopping mall showed that even novice PDA users accepted the system immediately and were able to achieve their shopping goals faster than when using a paper map of the mall. A key issue is the extent to which spontaneous user behavior can be accommodated within this framework.

An intelligent user interface sometimes needs to present a sequence of related recommendations to a user, in spite of being uncertain in advance as to whether (and with what success) the user will follow each recommendation. There are potential advantages to the use of decision-theoretic planning methods which yield an optimal policyfor the situation-dependent presentation of recommendations. This approach is discussed with reference to an example involving route instructions given by an airport assistance system.

How can an adaptive intelligent interface decide what particular action to perform in a given situation, as a function of perceived properties of the user and the situation? Ideally, such decisions should be made on the basis of an empirically derived causal model. In this paper we show how such a model can be constructed given an appropriately limited system and domain: On the basis of data from a controlled experiment, an influence diagram for making adaptation decisions is learned automatically. We then discuss why this method will often be infeasible in practice, and how parts of the method can nonetheless be used to create a more solid basis for adaptation decisions. Keywords Adaptive systems, Experiments, Decision theory, Influence diagrams, Bayesian networks INTRODUCTION One way in which an intelligent user interface can be intelligent is by adapting autonomously to properties of the user or the situation. A user-adaptive system can be defined as a system ( ) that (a) makes ...

Recent advances in user modeling technology have brought within reach the goal of having systems adapt to temporary limitations of the user’s available time and working memory capacity. We first summarize empirical research by ourselves and others that sheds light on the causes and consequences of these (continually changing) resource limitations. We then present a decision-theoretic approach that allows a system to assess a user’s resource limitations and to adapt its behavior accordingly. This approach is illustrated with reference to the performance of the prototype assistance system READY.