Hector Geffner

I am  a researcher at the Institucion Catalana de Recerca i Estudis Avançats (ICREA)
and a professor at the  Departamento de Tecnologia, Universitat Pompeu Fabra 
where I head the Artificial Intelligence Group

My mailing address is:

Hector Geffner
DTIC, Universitat Pompeu Fabra
C/Roc Boronat 138
E-08018 Barcelona, Spain

+34 93 542 2563 (voice)
+34 93 542 2451  (fax)

firstname.lastname@upf.edu

Index

• Teaching
• Software
• Opportunities
• Recent Publications
• Brief Biography

Teaching

• Slides

Software 

• Sofware and example files for each of the papers should be available from co-author pages; if not, feel free to request them by email
  

Opportunities

    If interested in joining our group as a PhD student, postdoc, or faculty, click  here. We can't take summer interns though.
 

Recent Papers, Reports, Slides

• Width and Serialization of Classical Planning Problems. N. Lipovetzky and H. Geffner. Proc. ECAI-2012, Montpellier. (Slides)
  
• Action Selection for MDPs: Anytime AO* vs. UCT. B. Bonet and H. Geffner. Proc. AAAI-2012, Toronto.
  
• Width and Complexity of Belief Tracking in Non-Deterministic Conformant and Contingent Planning. B. Bonet and H. Geffner. Proc. AAAI-2012.
  
• Planning under Partial Observability by Classical Replanning: Theory and Experiments. B. Bonet and H. Geffner. Proc. IJCAI-2011, Barcelona
  
• Goal Recognition over POMDPs: Inferring the Intention of a POMDP Agent. M. Ramirez and H. Geffner. Proc. IJCAI-2011, Barcelona
  
• Computing Infinite Plans for LTL Goals using a Classical Planner. F. Patrizi, N. Lipovetzky, G. De Giacomo, H. Geffner. Proc IJCAI-2011, Barcelona
• Advanced Introduction to Planning: Models and Methods, H. Geffner. Tutorial at IJCAI-2011, Barcelona
  
• Qualitative Numeric Planning. S. Srivastava, S. Zilberstein, N. Immerman, H. Geffner. Proc. AAAI-2011, San Francisco
  
• Effective Heuristics and Belief Tracking for Planning with Incomplete Information. A. Albore, M. Ramirez, H. Geffner. Proc. ICAPS-2011, Freiburg
• Searching for Plans with Carefully Designed Probes. N. Lipovetzky and H. Geffner. Proc. ICAPS-2011, Freiburg
• Heuristic Search for Generalized Stochastic Shortest Path MDPs. A. Kolobov, Mausam, D. Weld, H. Geffner, Proc. ICAPS-2011, Freiburg
• Planning and Plan Recognition (Slides), Invited talk, Dagstuhl Plan Recognition Seminar, 2011
  
• Heuristics with Choice Variables: Bridging the Gap between Planning and Graphical Models, E. Keyder, M. Ramirez, H. Geffner. ICAPS Workshop on Heuristics for Domain Independent Planning, 2011
• Course on Automated Planning (Slides). Univ. La Sapienza, Rome, Summer 2010
• The Model-based Approach to Autonomous Behavior: Prospects and Challenges (Slides), Invited talk, AI*IA, Brescia, 12/2010
  
• Heuristics, Probability and Causality: A Tribute to Judea Pearl. R. Dechter, H. Geffner, J. Halpern (Eds). College Publications, 2010 (Table of Contents)
• Heuristics, Planning, and Cognition. In Heuristics, Probability and Causality: A Tribute to Judea Pearl. R. Dechter, H. Geffner, J. Halpern (Eds), College Publications, 2010.
• Probabilistic Plan Recognition using off-the-shelf Classical Planners. M. Ramirez and H. Geffner. Proc. AAAI-10. Atlanta, USA. 7/2010
• The Model-based Approach to Autonomous Behavior: A Personal View. H. Geffner. Proc. AAAI-10. Atlanta, USA. 7/2010
• Automatic Derivation of Finite-State Machines for Behavior Control. B. Bonet, H. Palacios. H. Geffner. Proc. AAAI-10. Atlanta, USA. 7/2010
• Compiling Away Uncertainty in Non-Deterministic Conformant Planning Problems. A. Albore, H. Palacios, H. Geffner. Proc. ECAI-10, Lisbon, Portugal, 8/2010
• Soft Goals Can Be Compiled Away. E. Keyder and H. Geffner. Journal of Artificial Intelligence Research, Volume 36, pages 547-556, 2009.
• Inference and Learning in Planning (Extended Abstract). H. Geffner. Proc. Discovery Science 2009, Porto, Portugal, 10/2009.
• Automatic Derivation of Memoryless Policies and Finite-State Controllers Using Classical Planners. B. Bonet, H. Palacios, H. Geffner. Proc. ICAPS-09. Thessaloniki. Greece. 9/2009.
• Inference and Decomposition in Planning Using Causal Consistent Chains. N. Lipovetzky and H. Geffner. Proc. ICAPS-09. Thessaloniki. Greece. 9/2009 (Slides)
• Plan Recognition as Planning. M. Ramirez and H. Geffner. Proc. IJCAI-09. Pasadena, USA. 7/09.
• A Translation-based Approach to Contingent Planning. A. Albore, H. Palacios, H. Geffner. Proc. IJCAI-09. Pasadena, USA. 7/09.
• Trees of Shortest Paths vs. Steiner Trees: Understanding and Improving Delete Relaxation Heuristics. E. Keyder and H. Geffner. Proc. IJCAI-09. Pasadena, USA. 7/09.
• Solving POMDPs: RTDP-Bel vs. Point-based Algorithms. B. Bonet and H. Geffner. Proc. IJCAI-09. Pasadena, USA. 7/09.
• Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width. H. Palacios and H. Geffner Journal of AI Research (JAIR). To appear. 2009.
• Heuristics for Planning with Penalties and Rewards Formulated in Logic and Computed Through Circuits, B. Bonet and H. Geffner. Artificial Intelligence, 2008 (172) 1579-1604
• Heuristics for Planning with Action Costs Revisited. E. Keyder and H. Geffner. Proc. ECAI-08, Patras, Greece.7/2008.
• Unifying the Causal Graph and Additive Heuristics, M. Helmert and H. Geffner, Proc. 18Int. Conf. On Planning and Scheduling (ICAPS-08), Sydney, 9/2008
• AI at 50: From Programs to Solvers. Models and Techniques for General Intelligence (Slides). H. Geffner. University of Edinburgh, School of Informatics. Distinguished Lecture Series. 12/2007
• From Conformant into Classical Planning: Efficient Translations that may be Complete Too. H. Palacios and H. Geffner. Proc. 17th Int. Conf. on Planning and Scheduling (ICAPS-07), Providence, 9/2007
• Structural Relaxations by Variable Renaming and their Compilation for  Solving MinCostSAT. M. Ramirez and H. Geffner. Proc.  13th Int. Conf. on Principles and Practice of Constraint Programming (CP-07), Providence, 9/2007.
• The Causal Graph Heuristic is the Additive Heuristic plus Context. H. Geffner. Proc. 2007 ICAPS Workshop on Heuristics for Domain-Independent Planning. Providence, 9/2007
• Fast and Informed Action Selection for Planning with Sensing. A. Albore, H. Palacios, H. Geffner. Proc.  12th Conf.  Spanish AI (CAEPIA-07),  Springer,  Salamanca, 11/07.
• Set-Additive and TSP Heuristics for Planning with Action Costs and Soft Goals. E. Keyder and H. Geffner. Proc. 2007 ICAPS Workshop on Heuristics for Domain-Independent Planning. Providence, 9/2007
• Logical Encodings with No Time Indexes for Defining and Computing Admissible Heuristics for Planning. M. Ramirez, B. Bonet, H. Geffner. Proc. 2007 ICAPS Workshop on Heuristics for Domain-Independent Planning. Providence, 9/2007
• Heuristics for Planning with Action Costs. E. Keyder and H. Geffner. Proc.  12th Conf.  Spanish AI (CAEPIA-07),  Springer,  Salamanca, 11/07.
• Compiling Uncertainty Away: Solving Conformant Planning Problems Using a Classical Planner (Sometimes). H. Palacios and H. Geffner. Proc.  21st Nat. Conf. on Artificial Intelligence (AAAI-06), Boston, 7/2006
• Heuristics for Planning with Penalties and Rewards using Compiled Knowledge. B. Bonet and H. Geffner. Proc. 10th Int. Conf.  on Principles of Knowledge Representation and Reasoning (KR-06), Lake District, UK, 6/2006
• Learning Depth-First Search: A Unified Approach to Heuristic Search in Deterministic and Non-Deterministic Settings, and its application to MDPs. B. Bonet and H. Geffner. Proc. 16th Int. Conf. on Automated Planning and Scheduling (ICAPS-06), Lake District, UK, 6/2006
• Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming (Extended Version). V. Vidal and H. Geffner. Artificial Intelligence,  Vol 170(3), pp  298-335,  March 2006.
• mGPT: a Probabilistic Planner based on Heuristic Search. B. Bonet and H. Geffner. Journal of AI Research, Vol 24, pp 933-944, 2005.
• Search and Inference in AI Planning. H. Geffner. Proc. of the 11th Int. Conf. on Principles and Practice of Constraint Programming (CP-05). Sitges, Spain, Oct 2005. Invited talk (Slides)
• Mapping Conformant Planning into SAT through Compilation and Projection. H. Palacios and H. Geffner.  Lecture Notes in Computer Sciences, vol 4177.  Proc. CAEPIA-05  (originally in  Spanish)
• Learning in Depth-First Search: A Unified Approach to Heuristic Search in Deterministic, Non-Deterministic, Probabilistic, and Game Tree Settings. B. Bonet and H. Geffner. 2005.
• Solving Simple Planning Problems with More Inference and No Search, V. Vidal and H. Geffner. Proc. of the 11th Int. Conf. on Principles and Practice of Constraint Programming (CP-05). Sitges, Spain, Oct 2005.
• Selecting Actions and Making Decisions: Lessons from AI Planning. H. Geffner. Proc. IJCAI-05 Workshop on Modeling Natural Action Selection (MNAS-05). Edinburgh,  July 2005. (Slides)
• An Algorithm Better than AO*? B. Bonet and H. Geffner. Proc. 20th National Conf. on Artificial Intelligence (AAAI-05), Pittsburgh, July 2005, pp 1343-1348. (Slides)
• New Admissible Heuristics for Domain-Independent Planning. P. Haslum, B. Bonet and H. Geffner. Proc. 20th National Conf. on Artificial Intelligence (AAAI-05), Pittsburgh, July 2005. pp 1163-1168.
• Pruning Conformant Plans by Counting Models on Compiled d-DNNF Representations.H. Palacios, B. Bonet, A. Darwiche, H. Geffner. Proc. of the 15th Int. Conf. on Planning and Scheduling (ICAPS-05). Monterey, CA. AAAI Press, pp 141-150.  
• Learning Generalized Policies from Planning Examples Using Concept Languages (Extended and Revised Version).  M. Martin and H. Geffner, Applied Intelligence 20 (1) , 9-19, 2004
• Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. V. Vidal and H. Geffner.  Proc. 19th National Conference on Artificial Intelligence (AAAI-04), San Jose, CA, July 2004
• Planning Graphs and Knowledge Compilation. H. Geffner. Proc.  9th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR-04). Vancouver, June 2004  (Slides)
• PDDL 2.1: Representation vs. Computation.H. Geffner (a commentary on PDDL 2.1 and the 3rd Int. Planning Competition). J. of AI Research, vol 20, 2003.
• Faster Heuristic Search Algorithms for Planning with Uncertainty and Full Feedback. B. Bonet and H. Geffner. Int. Joint Conference on Artificial Intelligence (IJCAI-2003), Acapulco, Mexico, August 2003
• Labeled RTDP: Improving the Convergence of Real Time Dynamic Programming. B. Bonet and H. Geffner. 13th International Conference on Automated Planning and Scheduling (ICAPS-2003), Trento, Italy, June 2003
• Branching Matters: Alternative Branching in Graphplan. J. Hoffmann and H. Geffner. 13th International Conference on Automated Planning and Scheduling (ICAPS-2003), Trento, Italy, June 2003.
• Perspectives on Artificial Intelligence Planning. H. Geffner Proceedings Eighteenth National Conference on Artificial Intelligence (AAAI-2002), 2002, AAAI/MIT Press, pp 1013-1023. Invited talk (Slides)
• Planning as Branch and Bound: A Constraint Programming Implementation;  H. Palacios and H. Geffner. XVIII Latin-American Conference on Informatics (CLEI-2002), Montevideo, Uruguay, 11/2002
• Heuristic Search Planning: Progress and Challenges , Slides Invited Talk at European Conf. on Planning (ECP-2001), 9/2001, Toledo, Spain (4 slides per page)
• Planning as Branch and Bound and its Relation to Constraint-based Approaches;  H. Geffner, 4/2001
• GPT: A Tool for Planning with Uncertainty and Partial Information;  B. Bonet and H. Geffner;  IJCAI-2001 Workshop on Planning with Incomplete Info, 8/2001
• Planning and Control in Artificial Intelligence: a Unifying Perspective.  B. Bonet and H. Geffner. Applied Intelligence, Vol 14(3), 2001, pp  237--252.
• Heuristic Planning with Time and Resources;  P. Haslum and H. Geffner,  Proceedings ECP-2001  4/2001, Springer
• HSP2:  Decription HSP Planner  in AIPS-2000 Competition, B. Bonet and H. Geffner, AI Magazine Vol 22 (3), 2001, pp 77--80
• Planning as Heuristic Search  B. Bonet and H. Geffner.  Artificial Intelligence, Special issue on Heuristic Search. Vol 129 (1-2) 2001
• Planning with Incomplete Information as Heuristic Search in Belief Space B. Bonet and H. Geffner. Proc. 5th Int. Conf. on AI Planning and Scheduling (AIPS 2000), Colorado, 4/2000, AAAI Press, pp 52-61. (Slides)
• Admissible Heuristics for Optimal Planning P. Haslum H. Geffner. Proc. 5th Int. Conf. on AI Planning and Scheduling (AIPS 2000), Colorado, 4/2000, AAAI Press pp 140-149. (Slides)
• Learning generalized policies in planning using concept languages M. Martin and H. Geffner. Proc. 7th Int. Conf. on Knowledge Representation and Reasoning (KR 2000). Colorado, 4/2000, Morgan Kaufmann. (Slides)
• HSP: Heuristic Search Planner B. Bonet and H. Geffner. Entry at AIPS-98 Planning Competition, AI Magazine Vol 21(2), 2000
• Functional Strips: a more flexible language for planning and problem solving H. Geffner. In Logic-Based Artificial Intelligence, Jack Minker (Ed.), Kluwer, 2000. Earlier version presented at Logic-based AI Workshop, Washington D.C., June 1999.
• Planning as Heuristic Search: New Results B. Bonet and H. Geffner. Proc. European Conference on Planning (ECP-99), Durham, UK, Springer. (Slides)
• Modelling Intelligent Behaviour: the Markov Decision Approach H. Geffner. Invited talk Iberamia 98, Lect. Notes in AI 1484, H. Coelho (Ed), pp 1--12, Springer, 1998
• Classical, Probabilistic and Contingent Planning: Three Models, One Algorithm H. Geffner. Proceedings AIPS'98 Workshop on Planning as Combinatorial Search. (Slides)
• High-Level Planning and Control with Incomplete Information Using POMDPs H. Geffner and B. Bonet. Proceedings Fall AAAI Symposium on Cognitive Robotics, 1998. (Slides)
• Solving Large POMDPs by Real Time Dynamic Programming H. Geffner and B. Bonet. Working Notes Fall AAAI Symposium on POMDPS. 1998.
• Modeling action, knowledge and control H. Geffner and J. Wainer. Proceedings European Conference on Artificial Intelligence (ECAI-98), Brighton, UK. 8/98.
• Learning Sorting and Decision Trees with POMDPs B. Bonet and H. Geffner. Proceedings International Conference on Machine Learning (ICML-98), Madison, WI, 7/98. (Slides)
• Causality, Constraints and the Side Effects of Actions H. Geffner. Proceedings IJCAI-97, Naogoya, Japan, 8/97.
• A fast and robust action selection mechanism for planning B. Bonet, G. Loerincs, H. Geffner. Proceedings AAAI-97. Provide, RI, 7/97. (Slides)
• An Analysis of control programs using models of action. J. Ramirez and H. Geffner. AAAI-97 Workshop on Theories of Action, Planning and Control. 1997 (Slides) 
• Tutorial Slides: Modeling with Defaults, Causal and Temporal Reasoning (74 pages) H. Geffner. AAAI-97, IJCAI-97,FAPR-96,SBIA-95. 1997. (Slides compressed 8 into 1 ,References)

Biographical Information

Hector Geffner got his Ph.D at  UCLA  with a dissertation that was co-winner of the 1990 ACM Dissertation Award. He  then worked as Staff Research Member at the IBM T.J. Watson Research Center in NY, USA and at the Universidad Simon Bolivar, in Caracas, Venezuela. He is currently  a researcher at the ICREA and a professor at the Universitat Pompeu Fabra. He is Associate Editor of Artificial Intelligence, and the Journal of Artificial Intelligence Research, and a  Fellow of AAAI and ECCAI. He is author of the book ``Default Reasoning: Causal and Conditional Theories'' , MIT Press, 1992, editor of the book "Heuristics, Probability, and Causality: a Tribute to Judea Pearl" along with R. Decther and Joe Halpern, College Publications, 2010, and author of the book "Advanced Introduction to Planning: Models and Methods",  with Blai Bonet, forthcoming from Morgan and Claypool. He is interested in computational models of reasoning, action, planning, learning, and social interaction that are domain-general and can scale up.