Fields of expertise
Professor Faltings joined EPFL in 1987 as professor of Artificial Intelligence. He holds a PhD degree from the University of Illinois at Urbana-Champaign, and a diploma from the ETHZ. His research has spanned different areas of intelligent systems linked to model-based reasoning. In particular, he has contributed to qualitative spatial reasoning, case-based reasoning (especially for design problems), constraint satisfaction for design and logistics problems, multi-agent systems, and intelligent user interfaces. His current work is oriented towards multi-agent systems and social computing, using concepts of game theory, constraint optimization and machine learning. In 1999, Professor Faltings co-founded Iconomic Systems, a company that developed a new agent-based paradigm for travel e-commerce. He has since co-founded 5 other startup companies and advised several others. Prof. Faltings has published more than 150 refereed papers on his work, and participates regularly in program committees of all major conferences in the field. He has served as associate editor of of the major journals, including the Journal of Artificial Intelligence Research (JAIR) and the Artificial Intelligence Journal. From 1996 to 1998, he served as head of the computer science department.
Building INR 231
Tel. + 41 21 693 27 35
Multi-agent constraint optimization: we develop methods for optimizing the joint operation of multiple agents, taking into account considerations of communication complexity, uncertainty and privacy.
Truthful opinion polls: we develop game-theoretic methods to ensure that information provided by self-interested agents is truthful and accurate, in order to make the wisdom of the crowds more accurate.
Preference-based search (together with HCI group): we develop methods that allow people to find items that best satisfy their preferences in large collections of structured or semi-structured data. We consider scenarios ranging from recommender systems to search in databases using constraints.
Multi-agent learning: we consider systems where multiple agents learn from their own data sources, in scenarios such as wireless channel allocation, recommendation and reputation systems, and information security.
Boi Faltings' research is sponsored by the Swiss National Science Foundation, the Swiss Federal Commission for Technology Innovation (CTI), and the European Commission.
|R. Jurca and B. Faltings
Journal of Artificial Intelligence Research (JAIR), 34, 2009, pp. 209-253.
|Mechanisms for Making Crowds Truthful|
|R. Jurca and B. Faltings
Proceeddings of the 2008 ACM Conference on Electronic Commerce, July, 2008, pp. 119-128
|Truthful Opinions from the Crowds|
|A. Petcu and B. Faltings
Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI-07, Hyderabad, India, Jan, 2007, pp. 1452-1457
|MB-DPOP: A New Memory-Bounded Algorithm for Distributed Optimization|
|P. Viappiani, B. Faltings and P. Pu
Journal of Artificial Intelligence Research (JAIR), 27, 2006, pp. 465-503
|Preference-based Search using Example-Critiquing with Suggestions|
In Handbook of Constraint Programming, Foundations of Artificial Intelligence, Francesca Rossi, Peter van Beek, Toby Walsh (ed.), 2006, pp. 699-729
|Distributed Constraint Programming|
|C. Frei, B. Faltings, and M. Hamdi
IEEE Journal on Selected Areas in Communication, 23(2), February, 2005, pp. 304-320
|Resource Allocation in Communication Networks Using Abstraction and Constraint Satisfaction|
|B. Faltings and S. Macho-Gonzalez
Artificial Intelligence, 161 (1-2), January, 2005, pp. 181-208
|Open Constraint Programming|