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Fahiem Bacchus

Papers available on-line

Bayesian Inference and #SAT

  1. Using More Reasoning to Improve #SAT Solving, J. Davies and F. Bacchus,  National Conference on Artificial Intelligence (AAAI-07) , pages 185-190, 2007.
  2. Algorithms and Complexity Results for #Sat and Bayesian Inference, F. Bacchus, S. Dalmao, and T. Pitassi FOCS 2003 340-351, 2003.
  3. Combining Component Caching and Clause Learning for Effective Model Counting, T. Sang, F. Bacchus, P. Beame, H. Kautz, and T. Pitassi, presented at SAT 2004 9 pages.
  4. Value Elimination: Bayesian Inference via Backtracking Search, F. Bacchus, S. Dalmao, and T. Pitassi Uncertainty in Artificial Intelligence (UAI-2003) 20-28, 2003.

Planning

  1. A Heuristic Search Approach to Planning with Temporally Extended Preferences, J. A. Baier, F. Bacchus and S. McIlraith, International Joint Conference on Artificial Intelligence (IJCAI-07) pages 1808--1815, 2007.
  2. Extending the Knowledge-Based approach to Planning with Incomplete Information and Sensing, R. Petrick and F. Bacchus, International Conference on Automated Planning and Scheduling (ICAPS2004) pages 2-11, 2004.
  3. Utilizing Structured Representations and CSPs in Conformant Probabilistic Planning,  N. Hyafil and F. Bacchus, European Conference on Artificial Intelligence 2004. pages 1033-1034 (link points to a more comprehensible version).
  4. The Power of Modeling---a Response to PDDL2.1, F. Bacchus Journal of Artificial Intelligence Research (JAIR) Volume 20 pages 125-132, 2003.
  5. Generalizing GraphPlan by Formulating Planning as a CSP, A. Lopez and F. Bacchus International Joint Conference on Artificial Intelligence IJCAI-2003, pages 954-960, 2003.
  6. Conformant Probabilistic Planning via CSPs, N. Hyafil and F. Bacchus International Conference on Automated Planning and Scheduling (ICAPS-2003), pages 205-214, 2003.
  7. A Knowledge-Based Approach to Planning with Incomplete Information and Sensing, R. Petrick and F. Bacchus AI Planning and Scheduling (AIPS2002) pages 212-222, 2002.
  8. Planning with Resources and Concurrency: A Forward Chaining Approach, F. Bacchus and M. Ady, International Joint Conference on Artificial Intelligence (IJCAI-2001), pages 417-424, 2001.
  9. Inner and Outer Boundaries of Literals: A Mechanism for Computing Domain Specific Information, F. Bacchus and Cameron Bruce Fraser, AIPS-2000 Workshop on Analysing and Exploiting Domain Knowledge for Efficient Planning. 2000.
  10. Evaluating First Order Formulas---the foundation for a general Search Engine, F. Bacchus and M. Ady, unpublished manuscript 1999.
  11. Precondition Control, F. Bacchus and M. Ady, unpublished manuscript 1999.
  12. Using Temporal Logics to Express Search Control Knowledge for Planning, F. Bacchus and F. Kabanza, Artificial Intelligence volume 16, pages 123--191, 2000.
  13. Making Forward Chaining Relevant, F. Bacchus and Y. W. Teh, Artificial Intelligence Planning Systems (AIPS-98), pages 54-61, 1998.
  14. Modeling an Agent's Incomplete Knowledge during Planning and Execution, F. Bacchus and R. Petrick, Knowledge Represention and Reasoning, pages 432--443. 1998.
  15. Planning for Temporally Extended Goals, F. Bacchus and F. Kabanza, Annals of Mathematics and Artificial Intelligence, vol. 22, pages 5--27, 1998.
  16. Reasoning about Noisy Sensors and Effectors in the Situation Calculus, F. Bacchus, J. Y. Halpern, and H. J. Levesque, Artificial Intelligence vol 111, pages 171-208, 1999..
  17. Structured Solution Methods for Non-Markovian Decision Processes, F. Bacchus, C. Boutilier and A. Grove, National Conference on Artpificial Intelligence (AAAI-97), pages 112--117, 1997.
  18. Rewarding Behaviors, F. Bacchus, C. Boutilier and A. Grove, National Conference on Artificial Intelligence (AAAI-96), pages 1160--1167, 1996.
  19. Planning for Temporally Extended Goals, F. Bacchus and F. Kabanza, National Conference on Artificial Intelligence (AAAI-96), pages 1215--1222, 1996.
  20. Using Temporal Logic to Control Search in a Forward Chaining Planner, F. Bacchus and F. Kabanza, New Directions in Planning, M. Ghallab and A. Milani (Eds.) IOS Press, pages 141--153, 1996.
  21. Reasoning about Noisy Sensors in the Situation Calculus, F. Bacchus, J.Y. Halpern and H.J. Levesque, International Joint Conference on Artificial Intelligence (IJCAI-95), pages 1933--1940, 1995.
  22. Downward Refinement and the Efficiency of Hierarchical Problem Solving, F. Bacchus and Q. Yang, Artificial Intelligence vol. 71, pages 43--100, 1994.
  23. The Expected Value of Hierarchical Problem Solving, F. Bacchus and Q. Yang, National Conference on Artificial Intelligence (AAAI-92), pages 364--374, 1992.

Constraint Satisfaction and SAT

  1. Solution Directed Backjumping for QCSP, F. Bacchus and K. Stergiou,  International Conference on Principles and Practice of Constraint Programming (CP 2007) , pages 148-163, 2007.
  2. GAC via Unit Propagation, F. Bacchus,  International Conference on Principles and Practice of Constraint Programming (CP 2007) , pages 133-147, 2007.
  3. Using Expectation Maximization to Find Likely Assignments for Solving CSP's, E. Hsu, M. Kitching, F. Bacchus and S. McIlraith,  National Conference on Artificial Intelligence (AAAI-07) , pages 224-230, 2007.
  4. Symmetric Component Caching, M. Kitching and F. Bacchus,  International Joint Conference on Artificial Intelligence (IJCAI-07) , pages 118--124, 2007.
  5. Dynamically Partitioning for Solving QBF, H. Samulowitz and F. Bacchus,  Theory and Applications of Satisfiability Testing (SAT 2007), pages 215-229, 2007.
  6. Preprocessing QBF, H. Samulowitz, J. Davies and F. Bacchus,  International Conference on Principles and Practice of Constraint Programming (CP 2006), pages 514--529, 2006.
  7. Binary Clause Reasoning in QBF, H. Samulowitz and F. Bacchus,  Theory and Applications of Satisfiability Testing (SAT 2006), pages 353-367, 2006.
  8. Using SAT in QBF, H. Samulowitz and F. Bacchus,  International Conference on Principles and Practice of Constraint Programming  (CP-2005), pages 578-592, 2005.
  9. Propagating Logical Combinations of Constraints, F. Bacchus and T. Walsh, International Joint Conference on Artificial Intelligence (IJCAI-2005), pages 35--40.
  10. Generalized NoGoods in CSPs, G. Katsirelos and F. Bacchus, National Conference on Artificial Intelligence (AAAI-2005) pages 390-396, 2005.
  11. Solving Non-clausal Formulas with DPLL search,  C. Thiffault, F. Bacchus, and T. Walsh, Principles and Practice of Constraint Programming--CP 2004 pages 663--678, 2004.
  12. Unrestricted Nogood Recording in CSP Search, G. Katsirelos and F. Bacchus, Principles and Practice of Constraint Programming--CP 2003 pages  873-877, 2003.
  13. Effective Preprocessing with Hyper-Resolution and Equality Reduction, F. Bacchus and J. Winter, In Sat 2003 Lecture Notes in Computer Science 2919, pages 341-355
  14. Enhancing Davis Putnam with Extended Binary Clause Reasoning, F. Bacchus, National Conference on Artificial Intelligence (AAAI-2002) pages 613-619, 2002.
  15. Exploring the Computational Tradeoff of more Reasoning and Less Searching, F. Bacchus, Fifth International Symposium on Theory and Applications of Satisfiability Testing, pages 7-16, 2002.
  16. Binary vs. Non-Binary Constraints, F. Bacchus, X. Chen, P. van Beek, and T. Walsh, Artificial Intelligence vol 140, 1-37, 2002
  17. GAC on Conjunctions of Constraints, G. Katsirelos and F. Bacchus, Principles and Practice of Constraint Programming--CP 2001 pages 610-614, 2001.
  18. Extending Forward Checking, F. Bacchus, Principles and Practice of Constraint Programming--CP 2000, pages 35-51, 2000.
  19. A Uniform View of Backtracking, F. Bacchus, unpublished manuscript 2000.
  20. Looking Forward in Constraint Satisfaction Algorithms, F. Bacchus and A. Grove, unpublished manuscript, 1999.
  21. On the Conversion between Non-Binary and Binary Constraint Satisfaction Problems, F. Bacchus and P. van Beek, National Conference on Artificial Intelligence (AAAI-98), pages 311-318, 1998.
  22. On the Forward Checking Algorithm, F. Bacchus and A. Grove, Principles and Practice of Constraint Programming (CP-95), pages 292--309, 1995. Lecture Notes in Computer Science #976, Springer Verlag
  23. Dynamic Variable Ordering in CSPs, F. Bacchus and P. van Run, Principles and Practice of Constraint Programming (CP-95), pages 258--275, 1995. Lecture Notes in Computer Science #976, Springer Verlag.
  24. Domain Independent Heuristics in Hybrid Algorithms for CSPs, P. van Run, MMath thesis 1994 (under my supervision), Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
  25. Algorithms for Constraint Satisfaction Problems (CSPs), Z. Liu, MMath thesis 1998 (under my supervision), Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.

Logics for Probabilities/Non-Monotonic Reasoning

  1. From Statistical Knowledge Bases to Degrees of Belief, F. Bacchus, A. Grove, J.Y. Halpern, and D. Koller, Artificial Intelligence, vol. 87, pages 75--143, 1996.
  2. Forming Beliefs about a Changing World, F. Bacchus, A. Grove, J. Y. Halpern, and D. Koller, National Conference on Artificial Intelligence (AAAI-94), pages 222-229, 1994.
  3. Generating New Beliefs from Old, F. Bacchus, A. Grove, J. Y. Halpern, and D. Koller, Uncertainty in Artificial Intelligence (UAI-94), pages 37--45, 1994.
  4. Statistical Foundations for Default Reasoning, F. Bacchus, A. Grove, J. Y. Halpern, and D. Koller, International Joint Conference on Artificial Intelligence (IJCAI-93), pages 563--569, 1993.
  5. From Statistics to Beliefs, F. Bacchus, A. Grove, J. Y. Halpern, and D. Koller, National Conference on Artificial Intelligence (AAAI-92), pages 602--608, 1992.
  6. Default Reasoning From Statistics,, F. Bacchus, National Conference on Artificial Intelligence (AAAI-91), pages 392--398, 1991.
  7. LP---A Logic for Representing and Reasoning with Statistical Knowledge, F. Bacchus, Computational Intelligence, vol 6, pages 209--231, 1990.
  8. Probabilistic Belief Logics, F. Bacchus, Proceedings of European Conference on Artificial Intelligence (ECAI-90), pages 59--64, 1990.
  9. A Modest, but Semantically Well Founded, Inheritance Reasoner, F. Bacchus, Proceedings of International Joint Conference on AI (IJCAI-89), pages 1104--1109, 1989.
  10. Representing and Reasoning with Probabilistic Knowledge, M.I.T. Press, 1990.

Utility Theory

  1. UCP-Networks: A Directed Graphical Representation of Conditional Utilities, C. Boutilier, F. Bacchus and R. Brafman Uncertainty in Artificial Intelligence (UAI-2001))pages 56--64 2001.
  2. Independence and Qualitative Decision Theory, F. Bacchus and A. GroveAAAI Spring Symposium on Qualitative preferences in deliberation and practical reasoning)1997.
  3. Utility Independence in a Qualitative Decision Theory, F. Bacchus and A. Grove, Principles of Knowledge Representation and Reasoning (KR-96), pages 542--552, 1996.
  4. Graphical models for preference and utility, F. Bacchus and A. Grove, Uncertainty in Artificial Intelligence (UAI-95), pages 3--10, 1995.

Learning Bayes Nets

  1. Using New Data to Refine a Bayesian Network, W. Lam and F. Bacchus, Uncertainty in Artificial Intelligence (UAI-94), pages 383--390, 1994.
  2. Learning Bayesian Belief Networks: An Approach based on the MDL Principle, W. Lam and F. Bacchus, Computational Intelligence, vol. 10, pages 269--293, 1994.
  3. Using Causal Information and Local Measures to Learn Bayesian Networks, W. Lam and F. Bacchus Uncertainty in Artificial Intelligence (UAI-93), pages 243--250, 1993.
  4. Using First-Order Probability Logics for the Construction of Bayesian Networks, F. Bacchus, Uncertainty in Artificial Intelligence (UAI-94), pages 219--226, 1993.
  5. Learning Bayesian Belief Networks, W. Lam and F. Bacchus, Pacific Rim Conference on Artificial Intelligence (PRICAI-92), pages 1237--1243, 1992.

Knowledge Representation/Philosophy

  1. A Non-Reified Temporal Logic, F. Bacchus, J. Tenenberg, and J. Koomen, Artificial Intelligence, vol 52, pages 87--108, 1991.
  2. Against Conditionalization, F. Bacchus, H. Kyburg, and M. Thalos, Synthese, vol 85, pages 475--506, 1990.

 


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