Fahiem Bacchus
Papers available online
Bayesian Inference and #SAT

Using More Reasoning to Improve
#SAT Solving, J. Davies and F. Bacchus,
National Conference on Artificial Intelligence
(AAAI07) ,
pages 185190, 2007.

Algorithms and Complexity Results
for #Sat and Bayesian Inference,
F. Bacchus, S. Dalmao, and T. Pitassi FOCS 2003
340351, 2003.

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.

Value Elimination: Bayesian
Inference via Backtracking Search,
F. Bacchus, S. Dalmao, and T. Pitassi Uncertainty in Artificial
Intelligence (UAI2003) 2028, 2003.
Planning

A Heuristic Search
Approach to Planning with Temporally Extended Preferences,
J. A. Baier, F. Bacchus and S. McIlraith, International Joint
Conference on Artificial Intelligence (IJCAI07) pages 18081815, 2007.

Extending the
KnowledgeBased approach to Planning with Incomplete Information and
Sensing, R. Petrick and F. Bacchus, International Conference on
Automated Planning and Scheduling
(ICAPS2004) pages 211, 2004.

Utilizing Structured
Representations and CSPs in Conformant Probabilistic Planning, N. Hyafil and F. Bacchus,
European Conference on Artificial Intelligence 2004. pages 10331034 (link
points to a more comprehensible version).

The Power of Modelinga Response to PDDL2.1,
F. Bacchus Journal of Artificial Intelligence Research (JAIR)
Volume 20 pages 125132, 2003.

Generalizing GraphPlan by Formulating Planning as a CSP,
A. Lopez and F. Bacchus International Joint Conference on
Artificial Intelligence IJCAI2003, pages 954960, 2003.

Conformant Probabilistic
Planning via CSPs, N. Hyafil and F. Bacchus
International Conference on Automated Planning and Scheduling
(ICAPS2003), pages 205214, 2003.

A KnowledgeBased Approach to
Planning with Incomplete Information and Sensing,
R. Petrick and F. Bacchus AI Planning and Scheduling
(AIPS2002) pages 212222, 2002.

Planning with Resources and Concurrency:
A Forward Chaining Approach,
F. Bacchus and M. Ady, International Joint Conference on Artificial Intelligence (IJCAI2001), pages 417424, 2001.

Inner and Outer Boundaries of Literals: A Mechanism for Computing Domain Specific Information,
F. Bacchus and Cameron Bruce Fraser, AIPS2000 Workshop on Analysing and Exploiting Domain Knowledge for Efficient Planning. 2000.

Evaluating
First Order Formulasthe foundation for a general Search Engine,
F. Bacchus and M. Ady, unpublished manuscript 1999.

Precondition
Control, F. Bacchus and M. Ady, unpublished manuscript
1999.

Using
Temporal Logics to Express Search Control Knowledge for Planning,
F. Bacchus and F. Kabanza, Artificial Intelligence volume 16, pages 123191, 2000.
 Making
Forward Chaining Relevant, F. Bacchus and Y. W. Teh, Artificial
Intelligence Planning Systems (AIPS98), pages 5461, 1998.
 Modeling an
Agent's Incomplete Knowledge during Planning and Execution, F.
Bacchus and R. Petrick, Knowledge Represention and Reasoning, pages
432443. 1998.
 Planning for
Temporally Extended Goals, F. Bacchus and F. Kabanza, Annals of
Mathematics and Artificial Intelligence, vol. 22, pages 527, 1998.
 Reasoning about
Noisy Sensors and Effectors in the Situation Calculus, F. Bacchus,
J. Y. Halpern, and H. J. Levesque, Artificial Intelligence vol 111,
pages 171208, 1999..
 Structured
Solution Methods for NonMarkovian Decision Processes, F. Bacchus,
C. Boutilier and A. Grove, National Conference on Artpificial
Intelligence (AAAI97), pages 112117, 1997.
 Rewarding
Behaviors, F. Bacchus, C. Boutilier and A. Grove, National
Conference on Artificial Intelligence (AAAI96), pages 11601167,
1996.
 Planning for
Temporally Extended Goals, F. Bacchus and F. Kabanza, National
Conference on Artificial Intelligence (AAAI96), pages 12151222,
1996.
 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 141153, 1996.
 Reasoning
about Noisy Sensors in the Situation Calculus, F. Bacchus, J.Y.
Halpern and H.J. Levesque, International Joint Conference on Artificial
Intelligence (IJCAI95), pages 19331940, 1995.
 Downward
Refinement and the Efficiency of Hierarchical Problem Solving, F.
Bacchus and Q. Yang, Artificial Intelligence vol. 71, pages
43100, 1994.
 The Expected
Value of Hierarchical Problem Solving, F. Bacchus and Q. Yang, National
Conference on Artificial Intelligence (AAAI92), pages 364374, 1992.
Constraint Satisfaction and SAT

Solution Directed Backjumping for
QCSP, F. Bacchus and K. Stergiou,
International Conference on Principles and Practice of Constraint
Programming (CP 2007) , pages 148163, 2007.

GAC via Unit Propagation, F. Bacchus,
International Conference on Principles and Practice of Constraint
Programming (CP 2007) , pages 133147, 2007.

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
(AAAI07) , pages 224230, 2007.

Symmetric Component Caching,
M. Kitching and F. Bacchus,
International Joint Conference on Artificial Intelligence
(IJCAI07) , pages 118124, 2007.

Dynamically Partitioning for Solving QBF, H. Samulowitz and F.
Bacchus, Theory and Applications of Satisfiability Testing
(SAT 2007), pages 215229, 2007.

Preprocessing QBF,
H. Samulowitz, J. Davies and F. Bacchus, International Conference on Principles and Practice of Constraint
Programming (CP 2006), pages 514529, 2006.

Binary Clause Reasoning in QBF,
H. Samulowitz and F. Bacchus, Theory and Applications of
Satisfiability Testing (SAT 2006), pages 353367, 2006.

Using SAT in QBF, H. Samulowitz and F.
Bacchus, International Conference on Principles and Practice of
Constraint Programming (CP2005), pages 578592, 2005.

Propagating Logical Combinations of Constraints,
F. Bacchus and T. Walsh, International Joint Conference on Artificial
Intelligence (IJCAI2005), pages 3540.

Generalized NoGoods in CSPs, G.
Katsirelos and F. Bacchus, National Conference on Artificial
Intelligence (AAAI2005) pages 390396, 2005.

Solving Nonclausal Formulas with DPLL
search, C. Thiffault, F. Bacchus, and T. Walsh, Principles and
Practice of Constraint ProgrammingCP 2004 pages 663678, 2004.

Unrestricted Nogood Recording in
CSP Search, G. Katsirelos and F. Bacchus, Principles and Practice of
Constraint ProgrammingCP 2003 pages 873877, 2003.
 Effective Preprocessing with
HyperResolution and Equality Reduction, F. Bacchus and
J. Winter, In Sat 2003 Lecture Notes in Computer Science
2919, pages 341355

Enhancing Davis Putnam with Extended
Binary Clause Reasoning,
F. Bacchus, National Conference on Artificial
Intelligence (AAAI2002) pages 613619, 2002.

Exploring the Computational Tradeoff
of more Reasoning and Less Searching,
F. Bacchus, Fifth International Symposium on Theory and
Applications of Satisfiability Testing, pages 716, 2002.

Binary vs. NonBinary Constraints,
F. Bacchus, X. Chen, P. van Beek, and T. Walsh, Artificial
Intelligence vol 140, 137, 2002

GAC on Conjunctions of Constraints,
G. Katsirelos and F. Bacchus, Principles and Practice of Constraint
ProgrammingCP 2001 pages 610614, 2001.

Extending Forward Checking,
F. Bacchus, Principles and Practice of Constraint
ProgrammingCP 2000, pages 3551, 2000.

A Uniform View of
Backtracking,
F. Bacchus, unpublished manuscript 2000.
 Looking
Forward in Constraint Satisfaction Algorithms, F. Bacchus and A.
Grove, unpublished manuscript, 1999.
 On the
Conversion between NonBinary and Binary Constraint Satisfaction Problems,
F. Bacchus and P. van Beek, National Conference on Artificial
Intelligence (AAAI98), pages 311318, 1998.
 On the Forward
Checking Algorithm, F. Bacchus and A. Grove, Principles and
Practice of Constraint Programming (CP95), pages 292309, 1995. Lecture
Notes in Computer Science #976, Springer Verlag
 Dynamic
Variable Ordering in CSPs, F. Bacchus and P. van Run, Principles
and Practice of Constraint Programming (CP95), pages 258275, 1995. Lecture
Notes in Computer Science #976, Springer Verlag.
 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.
 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/NonMonotonic Reasoning
 From
Statistical Knowledge Bases to Degrees of Belief, F. Bacchus, A.
Grove, J.Y. Halpern, and D. Koller, Artificial Intelligence, vol.
87, pages 75143, 1996.
 Forming
Beliefs about a Changing World, F. Bacchus, A. Grove, J. Y.
Halpern, and D. Koller, National Conference on Artificial Intelligence
(AAAI94), pages 222229, 1994.
 Generating
New Beliefs from Old, F. Bacchus, A. Grove, J. Y. Halpern, and D.
Koller, Uncertainty in Artificial Intelligence (UAI94), pages
3745, 1994.
 Statistical
Foundations for Default Reasoning, F. Bacchus, A. Grove, J. Y.
Halpern, and D. Koller, International Joint Conference on Artificial
Intelligence (IJCAI93), pages 563569, 1993.
 From
Statistics to Beliefs, F. Bacchus, A. Grove, J. Y. Halpern, and D.
Koller, National Conference on Artificial Intelligence (AAAI92),
pages 602608, 1992.
 Default Reasoning From Statistics,,
F. Bacchus, National Conference on Artificial Intelligence (AAAI91),
pages 392398, 1991.
 LPA Logic
for Representing and Reasoning with Statistical Knowledge, F.
Bacchus, Computational Intelligence, vol 6, pages 209231, 1990.
 Probabilistic
Belief Logics, F. Bacchus, Proceedings of European Conference
on Artificial Intelligence (ECAI90), pages 5964, 1990.
 A Modest,
but Semantically Well Founded, Inheritance Reasoner, F. Bacchus, Proceedings
of International Joint Conference on AI (IJCAI89), pages 11041109,
1989.
 Representing
and Reasoning with Probabilistic Knowledge, M.I.T. Press,
1990.
Utility Theory

UCPNetworks: A Directed Graphical
Representation of Conditional Utilities, C. Boutilier,
F. Bacchus and R. Brafman Uncertainty
in Artificial Intelligence (UAI2001))pages 5664 2001.

Independence and Qualitative Decision Theory,
F. Bacchus and A. GroveAAAI Spring Symposium on Qualitative
preferences in deliberation and practical reasoning)1997.
 Utility Independence
in a Qualitative Decision Theory, F. Bacchus and A. Grove, Principles
of Knowledge Representation and Reasoning (KR96), pages 542552,
1996.
 Graphical
models for preference and utility, F. Bacchus and
A. Grove,
Uncertainty
in Artificial Intelligence (UAI95), pages 310, 1995.
Learning Bayes Nets
 Using New
Data to Refine a Bayesian Network, W. Lam and F. Bacchus, Uncertainty
in Artificial Intelligence (UAI94), pages 383390, 1994.
 Learning
Bayesian Belief Networks: An Approach based on the MDL Principle,
W. Lam and F. Bacchus, Computational Intelligence, vol. 10, pages
269293, 1994.
 Using Causal
Information and Local Measures to Learn Bayesian Networks, W. Lam
and F. Bacchus Uncertainty in Artificial Intelligence (UAI93),
pages 243250, 1993.
 Using
FirstOrder Probability Logics for the Construction of Bayesian Networks,
F. Bacchus, Uncertainty in Artificial Intelligence (UAI94), pages
219226, 1993.
 Learning
Bayesian Belief Networks, W. Lam and F. Bacchus, Pacific Rim
Conference on Artificial Intelligence (PRICAI92), pages 12371243,
1992.
Knowledge Representation/Philosophy
 A
NonReified Temporal Logic, F. Bacchus, J. Tenenberg, and J.
Koomen, Artificial Intelligence, vol 52, pages 87108, 1991.
 Against
Conditionalization, F. Bacchus, H. Kyburg, and M. Thalos, Synthese,
vol 85, pages 475506, 1990.
Back to my home page.