Context Aware Intelligent Assistance

CAIA  2010

Workshop held at the 33th Annual Conference on Artificial Intelligence (KI-2010)

September 21–24, 2010 Karlsruhe, Germany

Karlsruhe Institute of technology (KIT)

Sections

Welcome

Call for Papers

Important Dates

Submissions

Agenda

Organization


News


07.09.2010

Workshop agenda is announced

Accepted submissions online


15.07.2010

Submission deadline extended to 1st of August


23.04.2010

Website goes online

All the information about the workshop is available on this website!


A PDF version of the call is available here and a plain text version here

Call for Papers

Workshop Objectives

Topics of Interest

Topics include, but are not limited to:


  1. Formal models of preferences and contexts

  2. - Reasoning about preferences in contexts

  3. -Context modeling

  4. -User preference oriented route planning

  5. User needs and applications

  6. - location based services

  7. - social networks

  8. - navigation and planning of transportation

  9. Mobile recommendations

  10. - collaborative filtering

  11. - Interaction of social networks and mobile recommendation

  12. - Mobile feedback and interpretation of user tags

  13. - Semantic aggregation of web 2.0 information and services

  14. - Group recommendations

  15. Reasoning

  16. - Case based reasoning in mobile recommendation

  17. - Mobile speech technology and NLP

  18. - Dynamic environmental attributes (DEA)

  19. - Multiple goal recommendation

  20. - Event ontologies


Demos and applications are most welcome!

It is the goal of this workshop to bring together researchers from the fields of recommender systems, pervasive computing, mobile computing, urban sensing, social networking, context-aware systems and human computer interaction in order to foster the development of mobile services in context.

The main matters are:

  1. What is the nature of services provided to users on the move?

  2. How do needs and interests depend on contextual parameters?

  3. What levels of uncertainty have to be handled? / How is uncertainty handled?

  4. How can users configure and adapt systems’ recommendations / How are preferences handled?