05. – 08. Juni 2017, Dagstuhl Seminar 17232
Auskunft zu diesem Dagstuhl Seminar erteilen
Annette Beyer zu administrativen Fragen
Roswitha Bardohl zu wissenschaftlichen Fragen
(Zum Einloggen bitte Seminarnummer und Zugangscode verwenden)
The field of Human Computer Interaction (HCI) as a whole has been tremendously successful in the past, in terms of growth and impact of the premier academic conferences as well as in reshaping the IT industry. However, as we enter the post-PC era, new technologies emerge and bring along new challenges that the traditional user-centered design approach is not well equipped to meet. For example, artificial intelligence, wearable computing, augmented and virtual reality, and custom interactive devices enabled by emerging digital fabrication technologies pose increasingly substantial challenges for interaction design. Designers must consider the entire stack from low-level hardware through software all the way to the human factors. This implies that it is no longer feasible to disregard technology. Hence, we now have to deal with highly complex design spaces, rendering manual approaches infeasible.
In response to these challenges, researchers have begun to develop computational approaches to HCI problems based on machine learning, optimization, and formal methods. In the past, the domain of HCI was considered too complex to be tractable in traditional computational design settings. However, the scope of problems that can now be solved algorithmically, even on individual PCs, has expanded vastly due to advances in computing power and increased efficiency of numerical solvers. Thus, one goal of this Dagstuhl Seminar is to bring together researchers interested in exploring the algorithmic and computational aspects of HCI and interactive systems and to lay the foundations for new solutions to emerging challenges.
Despite the emergence of, and an increasing need for, computational methods to advance interactive systems for human use, research in this area is fragmented and splintered over a number of communities and forums. Such being the case, it is difficult to synthesize knowledge, to judge the current state-of-the-art, and to cooperate. Another goal of this Dagstuhl Seminar is to bring together researchers from different communities and disciplines, including machine-learning, data-mining, optimization, information retrieval, computer vision, graphics, cognitive and behavioral sciences, and HCI to lay the foundations for a future motor-theme in HCI and interactive system design. The meeting will not only allow for cross-fertilization between the various research directions, but it will also help to bridge gaps between foundational research on these topics and application-driven approaches.
The organizers intend to initiate new research directions for HCI that further establish computational approaches as a prime methodology in the field of HCI. The seminar also seeks to define and crystallize the main research challenges in this emerging area, and to define its current scope in relationship to adjacent fields. The organizers also aim to encourage researchers of relevant areas of computational and behavioural sciences to work directly on HCI problems, to define first viable solutions to score scientific problems in this area, and to provide a convenient point of reference, not only for the latest results, but also for the researchers currently active in this field. The organizers anticipate that the seminar will help in forming a more coherent community and may be the starting point for a future joint conference or symposium that brings together researchers that otherwise associate themselves with disjoint research communities.
This Dagstuhl Seminar will focus on the following topics: (i) machine inference of human behavior, (ii) computational design of interactive systems, and (iii) computational user-modeling.
Creative Commons BY 3.0 DE
Xiaojun Bi and Otmar Hilliges and Takeo Igarashi and Antti Oulasvirta
- Computer Graphics / Computer Vision
- Modelling / Simulation
- Society / Human-computer Interaction
- Machine learning