Hector Geffner

hgeffner's picture

Hector Geffner is an ICREA Research Professor at the Department of Information and Communication Technologies, UPF. He obtained a BSc on  Electrical Engineering at the Universidad Simon Bolivar in Caracas, and a MSc in Systems Science and a PhD in Computer Science  at the University of California, Los Angeles (UCLA). After his PhD, he worked at the IBM T.J. Watson Research Center in NY, USA from 1989 until 1992, and at the Universidad Simon Bolivar, in Caracas, from 1992 until 2001. He also taught at Stanford University, Aachen University of Technology, Linkoping University, Université Paul Sabatier, and the University of Edinburgh, among other places. Since 2001 he has been at the UPF in Barcelona, as an ICREA Research Professor where he heads the Artificial Intelligence (AI) group. 

Hector works on planning and plan recognition in intelligent systems, developing methods for generating and recognizing autonomous behavior automatically using model-based methods. In these methods, agents are not programmed by hand, but rather derive their behavior by solving a model of the interaction between the agent, the environment, and possibly other agents. One of the main challenges in planning is computational as these models are all intractable in the worst case, and algorithms must be able to automatically recognize and exploit the structure of problems. The work involves logical and probabilistic models, domain-independent heuristics and algorithms, and computational experiments.

Hector's research is relevant to both artificial intelligence and cognitive science, as it aims to uncover general principles of rational behavior that take into account the computational constraints that are present in both natural and artificial systems. While his main interests are in AI and Cognitive Science, he is also quite interested in the Human and Social Sciences. He is currently involved in several funded research projects, basic and applied, including Simulpast, a Consolider Project about Simulating the Past that involves a number of archeologists; Spacebook, a European Project aiming at the development of speech-driven, hands-free, eyes-free devices for pedestrian navigation and exploration, and a National I+D Project, about robust and scalable model-based methods for the generation of autonomous behavior.

Hector Geffner is the recipient of the 1990 ACM Dissertation Award, and is best known for the heuristic search approach to planning for which he received the 2009 and 2010 ICAPS Influential Paper Awards. He is a fellow of both the American and  the European Association for Artificial Intelligence (AAAI, ECCAI), and  Associate Editor of the two top AI journals: Artificial Intelligence (AIJ), and the Journal of Artificial Intelligence Research (JAIR). He is  professionally involved in many of the top AI conferences, having served in recent years as Area Chair for  IJCAI, AAAI, ECAI, UAI, and KR, and as co-chair of ICAPS. Hector is the author of the book Default Reasoning, MIT Press, 1992, and co-editor with Rina Dechter and Joseph Halpern of the book Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publications, 2010. Most recently, he has also co-authored with Blai Bonet the book A Concise Introduction to Models and Methods for Automated Planning, Morgan&Claypool, 2013. He is currently the Director of the UPF Master on Intelligent Interactive Systems.


E. Keyder and Geffner, H., Set-Additive and TSP Heuristics for Planning with Action Costs and Soft Goals, in Workshop on Heuristics for Domain-Independent Planning (ICAPS'07), 2007.
A. Albore, Palacios, H., and Geffner, H., Fast and Informed Action Selection for Planning with Sensing, in Current Topics in Artificial Intelligence, 12th Conference of the Spanish Association for Artificial Intelligence (CAEPIA). Selected Papers, vol. 4788, D. Borrajo, Castillo, L., and Corchado, J. Manuel Salamanca, Spain: Springer, 2007. Paper CAEPIA 2007 (144.71 KB)
V. Vidal and Geffner, H., Solving simple planning problems with more inference and no search, in Proc. of the 11th Int. Conf. on Principles and Practice of Constraint Programming (CP-05), 2005.
V. Vidal and Geffner, H., Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming, in Proceedings of 19th Nat. Conf. on Artificial Intelligence (AAAI-04), 2004, pp. 570-577. Paper AIJ 2006 (291.11 KB)