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Papers

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Leveraging dominators for preprocessing QBF. Mangassarian, H.; Le, B.; Goultiaeva, A.; Veneris, A. G.; and Bacchus, F. 2010. In Design, Automation and Test in Europe (DATE 2010), 1695-1700.
Leveraging dominators for preprocessing QBF [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5457088] Leveraging dominators for preprocessing QBF [PDF] Leveraging dominators for preprocessing QBF [bib]
Exploiting QBF Duality on a Circuit Representation. Goultiaeva, A., and Bacchus, F. 2010. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-2010), 71-76.
Exploiting QBF Duality on a Circuit Representation [http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1791] Exploiting QBF Duality on a Circuit Representation [PDF] Exploiting QBF Duality on a Circuit Representation [bib]
Using Learnt Clauses in MAXSAT. Davies, J.; Cho, J.; and Bacchus, F. 2010. In 16th International Conference on Principles and Practice of Constraint Programming (CP-2010), 176-190.
Using Learnt Clauses in MAXSAT [PDF] Using Learnt Clauses in MAXSAT [bib]
Exploiting Circuit Representations in QBF Solving. Goultiaeva, A., and Bacchus, F. 2010. In Proceedings of the 13th International Conference on Theory and Applications of Satisfiability Testing (SAT-2010), 333-339.
Exploiting Circuit Representations in QBF Solving [http://dx.doi.org/10.1007/978-3-642-14186-7_29] Exploiting Circuit Representations in QBF Solving [PDF] Exploiting Circuit Representations in QBF Solving [bib]
Exploiting Decomposition on Constraint Problems with High Tree-Width. Kitching, M., and Bacchus, F. 2009. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-2009), 525-531.
Exploiting Decomposition on Constraint Problems with High Tree-Width [PDF] Exploiting Decomposition on Constraint Problems with High Tree-Width [bib]
Set Branching in Constraint Optimization. Kitching, M., and Bacchus, F. 2009. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-2009), 532-537.
Set Branching in Constraint Optimization [PDF] Set Branching in Constraint Optimization [bib]
Beyond CNF: A Circuit-Based QBF Solver. Goultiaeva, A.; Iverson, V.; and Bacchus, F. 2009. In Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing (SAT-2009), 412-426.
Beyond CNF: A Circuit-Based QBF Solver [http://dx.doi.org/10.1007/978-3-642-02777-2_38] Beyond CNF: A Circuit-Based QBF Solver [PDF] Beyond CNF: A Circuit-Based QBF Solver [bib]
A heuristic search approach to planning with temporally extended preferences. Baier, J. A.; Bacchus, F.; and McIlraith, S. A. 2009. 173(5--6):593-618.
A heuristic search approach to planning with temporally extended preferences [http://dx.doi.org/10.1016/j.artint.2008.11.011] A heuristic search approach to planning with temporally extended preferences [bib]
Solving #SAT and Bayesian Inference with Backtracking Search. Bacchus, F.; Dalmao, S.; and Pitassi, T. 2009. J. Artif. Intell. Res. (JAIR), 34:391-442.
Solving #SAT and Bayesian Inference with Backtracking Search [http://dx.doi.org/10.1613/jair.2648] Solving #SAT and Bayesian Inference with Backtracking Search [bib]
Exploiting Decomposition in Constraint Optimization Problems. Kitching, M., and Bacchus, F. 2008. In Proceedings of the 14th International Conference on Principles and Practice of Constraint Programming (CP-2008), 478-492.
Exploiting Decomposition in Constraint Optimization Problems [http://dx.doi.org/10.1007/978-3-540-85958-1_32] Exploiting Decomposition in Constraint Optimization Problems [PDF] Exploiting Decomposition in Constraint Optimization Problems [bib]
Clause Learning Can Effectively P-Simulate General Propositional Resolution. Hertel, P.; Bacchus, F.; Pitassi, T.; and Gelder, A. V. 2008. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-2008), 283-290.
Clause Learning Can Effectively P-Simulate General Propositional Resolution [PDF] Clause Learning Can Effectively P-Simulate General Propositional Resolution [bib]
Distributional Importance Sampling for Approximate Weighted Model Counting. Davies, J., and Bacchus, F. 2008. In Workshop on Counting Problems in CSP and SAT, and other neighbouring problems.
Distributional Importance Sampling for Approximate Weighted Model Counting [PDF] Distributional Importance Sampling for Approximate Weighted Model Counting [bib]
Dynamically Partitioning for Solving QBF. Samulowitz, H., and Bacchus, F. 2007. In Proceedings of the 10th International Conference on Theory and Applications of Satisfiability Testing (SAT-2007), 215-229.
Dynamically Partitioning for Solving QBF [http://dx.doi.org/10.1007/978-3-540-72788-0_22] Dynamically Partitioning for Solving QBF [PDF] Dynamically Partitioning for Solving QBF [bib]
Using Expectation Maximization to Find Likely Assignments for Solving CSP's. Hsu, E. I.; Kitching, M.; Bacchus, F.; and McIlraith, S. A. 2007. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), 224-230.
Using Expectation Maximization to Find Likely Assignments for Solving CSP's [PDF] Using Expectation Maximization to Find Likely Assignments for Solving CSP's [bib]
Symmetric Component Caching. Kitching, M., and Bacchus, F. 2007. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), 118-124.
Symmetric Component Caching [PDF] Symmetric Component Caching [PDF] Symmetric Component Caching [bib]
Using More Reasoning to Improve #SAT Solving. Davies, J., and Bacchus, F. 2007. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), 185-190.
Using More Reasoning to Improve #SAT Solving [PDF] Using More Reasoning to Improve #SAT Solving [bib]
Solution Directed Backjumping for QCSP. Bacchus, F., and Stergiou, K. 2007. In Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP-2007), 148-163.
Solution Directed Backjumping for QCSP [http://dx.doi.org/10.1007/978-3-540-74970-7_13] Solution Directed Backjumping for QCSP [PDF] Solution Directed Backjumping for QCSP [bib]
Caching in Backtracking Search (Invited Talk). Bacchus, F. 2007. In Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP-2007), 1-1.
Caching in Backtracking Search (Invited Talk) [http://dx.doi.org/10.1007/978-3-540-74970-7_1] Caching in Backtracking Search (Invited Talk) [PPT] Caching in Backtracking Search (Invited Talk) [bib]
A Heuristic Search Approach to Planning with Temporally Extended Preferences. Baier, J. A.; Bacchus, F.; and McIlraith, S. A. 2007. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), 1808-1815.
A Heuristic Search Approach to Planning with Temporally Extended Preferences [PDF] A Heuristic Search Approach to Planning with Temporally Extended Preferences [PDF] A Heuristic Search Approach to Planning with Temporally Extended Preferences [bib]
GAC Via Unit Propagation. Bacchus, F. 2007. In Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP-2007), 133-147.
GAC Via Unit Propagation [http://dx.doi.org/10.1007/978-3-540-74970-7_12] GAC Via Unit Propagation [PDF] GAC Via Unit Propagation [bib]
CSPs: Adding Structure to SAT (Invited Talk). Bacchus, F. 2006. In Proceedings of the 9th International Conference on Theory and Applications of Satisfiability Testing (SAT-2006), 10-10.
CSPs: Adding Structure to SAT (Invited Talk) [http://dx.doi.org/10.1007/11814948_2] CSPs: Adding Structure to SAT (Invited Talk) [PPT] CSPs: Adding Structure to SAT (Invited Talk) [bib]
Preprocessing QBF. Samulowitz, H.; Davies, J.; and Bacchus, F. 2006. In Proceedings of the 12th International Conference on Principles and Practice of Constraint Programming (CP-2006), 514-529.
Preprocessing QBF [http://dx.doi.org/10.1007/11889205_37] Preprocessing QBF [PDF] Preprocessing QBF [bib]
Binary Clause Reasoning in QBF. Samulowitz, H., and Bacchus, F. 2006. In Proceedings of the 9th International Conference on Theory and Applications of Satisfiability Testing (SAT-2006), 353-367.
Binary Clause Reasoning in QBF [http://dx.doi.org/10.1007/11814948_33] Binary Clause Reasoning in QBF [PDF] Binary Clause Reasoning in QBF [bib]
Using SAT in QBF. Samulowitz, H., and Bacchus, F. 2005. In Proceedings of the 11th International Conference on Principles and Practice of Constraint Programming (CP-2005), 578-592.
Using SAT in QBF [http://dx.doi.org/10.1007/11564751_43] Using SAT in QBF [PDF] Using SAT in QBF [bib]
Propagating Logical Combinations of Constraints. Bacchus, F., and Walsh, T. 2005. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-2005), 35-40.
Propagating Logical Combinations of Constraints [PDF] Propagating Logical Combinations of Constraints [PDF] Propagating Logical Combinations of Constraints [bib]
Generalized NoGoods in CSPs. Katsirelos, G., and Bacchus, F. 2005. In Proceedings of the 20th AAAI Conference on Artificial Intelligence (AAAI-2005), 390-396.
Generalized NoGoods in CSPs [PDF] Generalized NoGoods in CSPs [bib]
21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, Edinburgh, Scotland. Bacchus, F., and Jaakkola, T. 2005. Brightdoc On-Line Demand Publishers.
21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, Edinburgh, Scotland [https://pos.brightdoc.com/store/product.asp?ProductID=3020&CollectionID=0&TSearch=&DSearch=&ProductSelect=NotSelected&CategoryID=0&Cart=&page=1] 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, Edinburgh, Scotland [bib] Buy
Theory and Applications of Satisfiability Testing, 8th International Conference, SAT 2005, St Andrews, UK, June 19-23, 2005. Bacchus, F., and Walsh, T. 2005. Springer, Lecture Notes in Computer Science, 3569, 3-540-26276-8.
Theory and Applications of Satisfiability Testing, 8th International Conference, SAT 2005, St Andrews, UK, June 19-23, 2005 [http://dx.doi.org/10.1007/b137280] Theory and Applications of Satisfiability Testing, 8th International Conference, SAT 2005, St Andrews, UK, June 19-23, 2005 [bib] Buy
Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing. Petrick, R. P. A., and Bacchus, F. 2004. In Proceedings of the 9th International Conference on Principles of Knowledge Representation and Reasoning (KR-2004), 613-622.
Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing [PDF] Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing [bib]
Solving Non-clausal Formulas with DPLL Search. Thiffault, C.; Bacchus, F.; and Walsh, T. 2004. In Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming (CP-2004), 663-678.
Solving Non-clausal Formulas with DPLL Search [http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3258&spage=663] Solving Non-clausal Formulas with DPLL Search [PDF] Solving Non-clausal Formulas with DPLL Search [bib]
Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing. Petrick, R. P. A., and Bacchus, F. 2004. In Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS-2004), 2-11.
Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing [PDF] Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing [bib]
Utilizing Structured Representations and CSP's in Conformant Probabilistic Planning. Hyafil, N., and Bacchus, F. 2004. In Proceedings of the 16th Eureopean Conference on Artificial Intelligence (ECAI-2004), 1033-1034.
Utilizing Structured Representations and CSP's in Conformant Probabilistic Planning [PDF] Utilizing Structured Representations and CSP's in Conformant Probabilistic Planning [bib]
Combining Component Caching and Clause Learning for Effective Model Counting. Sang, T.; Bacchus, F.; Beame, P.; Kautz, H. A.; and Pitassi, T. 2004. In Proceedings of the 7th International Conference on Theory and Applications of Satisfiability Testing (SAT-2004).
Combining Component Caching and Clause Learning for Effective Model Counting [PDF] Combining Component Caching and Clause Learning for Effective Model Counting [PDF] Combining Component Caching and Clause Learning for Effective Model Counting [bib]
Value Elimination: Bayesian Inference via Backtracking Search. Bacchus, F.; Dalmao, S.; and Pitassi, T. 2003. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence August (UAI-2003), 20-28.
Value Elimination: Bayesian Inference via Backtracking Search [PDF] Value Elimination: Bayesian Inference via Backtracking Search [bib]
Generalizing GraphPlan by Formulating Planning as a CSP. Lopez, A., and Bacchus, F. 2003. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-2003), 954-960.
Generalizing GraphPlan by Formulating Planning as a CSP [PDF] Generalizing GraphPlan by Formulating Planning as a CSP [bib]
Effective Preprocessing with Hyper-Resolution and Equality Reduction. Bacchus, F., and Winter, J. 2003. In Proceedings of the 6th International Conference on Theory and Applications of Satisfiability Testing (SAT-2003), 341-355.
Effective Preprocessing with Hyper-Resolution and Equality Reduction [http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2919&spage=341] Effective Preprocessing with Hyper-Resolution and Equality Reduction [PDF] Effective Preprocessing with Hyper-Resolution and Equality Reduction [bib]
The Power of Modeling - a Response to PDDL2.1. Bacchus, F. 2003. J. Artif. Intell. Res. (JAIR), 20:125-132.
The Power of Modeling - a Response to PDDL2.1 [HTML] The Power of Modeling - a Response to PDDL2.1 [http://dx.doi.org/10.1613/jair.1993] The Power of Modeling - a Response to PDDL2.1 [bib]
Conformant Probabilistic Planning via CSPs. Hyafil, N., and Bacchus, F. 2003. In Proceedings of the 13th International Conference on Automated Planning and Scheduling (ICAPS-2003), 205-214.
Conformant Probabilistic Planning via CSPs [PDF] Conformant Probabilistic Planning via CSPs [bib]
Algorithms and Complexity Results for #SAT and Bayesian Inference. Bacchus, F.; Dalmao, S.; and Pitassi, T. 2003. In Proceedings of the 44th Symposium on Foundations of Computer Science (FOCS-2003), 340-351.
Algorithms and Complexity Results for #SAT and Bayesian Inference [http://csdl.computer.org/comp/proceedings/focs/2003/2040/00/20400340abs.htm] Algorithms and Complexity Results for #SAT and Bayesian Inference [PDF] Algorithms and Complexity Results for #SAT and Bayesian Inference [bib]
Unrestricted Nogood Recording in CSP Search. Katsirelos, G., and Bacchus, F. 2003. In Proceedings of the 9th International Conference on Principles and Practice of Constraint Programming (CP-2003), 873-877.
Unrestricted Nogood Recording in CSP Search [http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2833&spage=873] Unrestricted Nogood Recording in CSP Search [PDF] Unrestricted Nogood Recording in CSP Search [bib]
Binary vs. non-binary constraints. Bacchus, F.; Chen, X.; Beek, P. van ; and Walsh, T. 2002. Artif. Intell., 140(1/2):1-37.
Binary vs. non-binary constraints [http://dx.doi.org/10.1016/S0004-3702(02)00210-2] Binary vs. non-binary constraints [bib]
Enhancing Davis Putnam with Extended Binary Clause Reasoning. Bacchus, F. 2002. In Proceedings of the 18th AAAI Conference on Artificial Intelligence (AAAI-2002), 613-619.
Enhancing Davis Putnam with Extended Binary Clause Reasoning [PDF] Enhancing Davis Putnam with Extended Binary Clause Reasoning [bib]
A Knowledge-Based Approach to Planning with Incomplete Information and Sensing. Petrick, R. P. A., and Bacchus, F. 2002. In Proceedings of the 6th International Conference on Artificial Intelligence Planning Systems (AIPS-2002), 212-222.
A Knowledge-Based Approach to Planning with Incomplete Information and Sensing [PDF] A Knowledge-Based Approach to Planning with Incomplete Information and Sensing [bib]
The AIPS '00 Planning Competition. Bacchus, F. 2001. AI Magazine, 22(3):47-56.
The AIPS '00 Planning Competition [http://www.aaai.org/ojs/index.php/aimagazine/article/view/1571] The AIPS '00 Planning Competition [bib]
Planning with Resources and Concurrency: A Forward Chaining Approach. Bacchus, F., and Ady, M. 2001. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-2001), 417-424.
Planning with Resources and Concurrency: A Forward Chaining Approach [PDF] Planning with Resources and Concurrency: A Forward Chaining Approach [bib]
UCP-Networks: A Directed Graphical Representation of Conditional Utilities. Boutilier, C.; Bacchus, F.; and Brafman, R. I. 2001. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (UAI-2001), 56-64.
UCP-Networks: A Directed Graphical Representation of Conditional Utilities [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=83&proceedingID=17] UCP-Networks: A Directed Graphical Representation of Conditional Utilities [PDF] UCP-Networks: A Directed Graphical Representation of Conditional Utilities [bib]
GAC on Conjunctions of Constraints. Katsirelos, G., and Bacchus, F. 2001. In Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming (CP-2001), 610-614.
GAC on Conjunctions of Constraints [http://link.springer.de/link/service/series/0558/bibs/2239/22390610.htm] GAC on Conjunctions of Constraints [PDF] GAC on Conjunctions of Constraints [bib]
Extending Forward Checking. Bacchus, F. 2000. In Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming (CP-2000), 35-51.
Extending Forward Checking [http://link.springer.de/link/service/series/0558/bibs/1894/18940035.htm] Extending Forward Checking [PDF] Extending Forward Checking [bib]
Using temporal logics to express search control knowledge for planning. Bacchus, F., and Kabanza, F. 2000. Artif. Intell., 116(1-2):123-191.
Using temporal logics to express search control knowledge for planning [http://dx.doi.org/10.1016/S0004-3702(99)00071-5] Using temporal logics to express search control knowledge for planning [http://dx.doi.org/10.1016/S0004-3702(99)00071-5] Using temporal logics to express search control knowledge for planning [bib]
Inner and Outer Boundaries of Literals: A Mechanism for Computing Domain Specific Information. Bacchus, F., and Fraser, C. B. 2000. In AIPS-2000 Workshop on Analysing and Exploiting Domain Knowledge for Efficient Planning.
Inner and Outer Boundaries of Literals: A Mechanism for Computing Domain Specific Information [PDF] Inner and Outer Boundaries of Literals: A Mechanism for Computing Domain Specific Information [bib]
Evaluating First Order Formulas---the foundation for a general Search Engine. Bacchus, F., and Ady, M. 1999. Unpublished.
Evaluating First Order Formulas---the foundation for a general Search Engine [PDF] Evaluating First Order Formulas---the foundation for a general Search Engine [bib]
Reasoning about Noisy Sensors and Effectors in the Situation Calculus. Bacchus, F.; Halpern, J. Y.; and Levesque, H. J. 1999. Artif. Intell., 111(1-2):171-208.
Reasoning about Noisy Sensors and Effectors in the Situation Calculus [http://dx.doi.org/10.1016/S0004-3702(99)00031-4] Reasoning about Noisy Sensors and Effectors in the Situation Calculus [bib]
Precondition Control. Bacchus, F., and Ady, M. 1999. Unpublished.
Precondition Control [PDF] Precondition Control [bib]
Modeling an Agent's Incomplete Knowledge During Planning and During Execution. Bacchus, F., and Petrick, R. P. A. 1998. In Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning (KR-1998), 432-443.
Modeling an Agent's Incomplete Knowledge During Planning and During Execution [PDF] Modeling an Agent's Incomplete Knowledge During Planning and During Execution [bib]
On the Conversion between Non-Binary and Binary Constraint Satisfaction Problems. Bacchus, F., and Beek, P. van 1998. In Proceedings of the 15th AAAI Conference on Artificial Intelligence (AAAI-1998), 310-318.
On the Conversion between Non-Binary and Binary Constraint Satisfaction Problems [PDF] On the Conversion between Non-Binary and Binary Constraint Satisfaction Problems [bib]
Planning for Temporally Extended Goals. Bacchus, F., and Kabanza, F. 1998. Ann. Math. Artif. Intell., 22(1-2):5-27.
Planning for Temporally Extended Goals [PDF] Planning for Temporally Extended Goals [bib]
Making Forward Chaining Relevant. Bacchus, F., and Teh, Y. W. 1998. In Proceedings of the 2nd International Conference on Artificial Intelligence Planning Systems (AIPS-1998), 54-61.
Making Forward Chaining Relevant [PDF] Making Forward Chaining Relevant [bib]
Structured Solution Methods for Non-Markovian Decision Processes. Bacchus, F.; Boutilier, C.; and Grove, A. J. 1997. In Proceedings of the 14th AAAI Conference on Artificial Intelligence (AAAI-1997), 112-117.
Structured Solution Methods for Non-Markovian Decision Processes [PDF] Structured Solution Methods for Non-Markovian Decision Processes [bib]
Utility Independence in a Qualitative Decision Theory. Bacchus, F., and Grove, A. J. 1996. In Proceedings of the 5th International Conference on Principles of Knowledge Representation and Reasoning (KR-1996), 542-552.
Utility Independence in a Qualitative Decision Theory [PDF] Utility Independence in a Qualitative Decision Theory [bib]
Planning for Temporally Extended Goals. Bacchus, F., and Kabanza, F. 1996. In Proceedings of the 13th AAAI Conference on Artificial Intelligence (AAAI-1996), 1215-1222.
Planning for Temporally Extended Goals [PDF] Planning for Temporally Extended Goals [bib]
From Statistical Knowledge Bases to Degrees of Belief. Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1996. Artif. Intell., 87(1-2):75-143.
From Statistical Knowledge Bases to Degrees of Belief [http://dx.doi.org/10.1016/S0004-3702(96)00003-3] From Statistical Knowledge Bases to Degrees of Belief [bib]
Using Temporal Logics for Planning and Control. Bacchus, F. 1996. In Proceedings of the 3rd International Workshop on Temporal Representation and Reasoning (TIME-1996), 2-3.
Using Temporal Logics for Planning and Control [http://doi.ieeecomputersociety.org/10.1109/TIME.1996.555666] Using Temporal Logics for Planning and Control [bib]
Rewarding Behaviors. Bacchus, F.; Boutilier, C.; and Grove, A. J. 1996. In Proceedings of the 13th AAAI Conference on Artificial Intelligence (AAAI-1996), 1160-1167.
Rewarding Behaviors [PDF] Rewarding Behaviors [bib]
On the Forward Checking Algorithm. Bacchus, F., and Grove, A. J. 1995. In Proceedings of the 1st International Conference on Principles and Practice of Constraint Programming (CP-1995), 292-308.
On the Forward Checking Algorithm [PDF] On the Forward Checking Algorithm [bib]
Dynamic Variable Ordering in CSPs. Bacchus, F., and Run, P. van 1995. In Proceedings of the 1st International Conference on Principles and Practice of Constraint Programming (CP-1995), 258-275.
Dynamic Variable Ordering in CSPs [PDF] Dynamic Variable Ordering in CSPs [bib]
Reasoning about Noisy Sensors in the Situation Calculus. Bacchus, F.; Halpern, J. Y.; and Levesque, H. J. 1995. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-1995), 1933-1940.
Reasoning about Noisy Sensors in the Situation Calculus [PDF] Reasoning about Noisy Sensors in the Situation Calculus [bib]
Graphical models for preference and utility. Bacchus, F., and Grove, A. J. 1995. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-1995), 3-10.
Graphical models for preference and utility [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=413&proceedingID=11] Graphical models for preference and utility [PDF] Graphical models for preference and utility [bib]
Downward Refinement and the Efficiency of Hierarchical Problem Solving. Bacchus, F., and Yang, Q. 1994. Artif. Intell., 71(1):43-100.
Downward Refinement and the Efficiency of Hierarchical Problem Solving [PDF] Downward Refinement and the Efficiency of Hierarchical Problem Solving [bib]
Using New Data to Refine a Bayesian Network. Lam, W., and Bacchus, F. 1994. In Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1994), 383-390.
Using New Data to Refine a Bayesian Network [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=527&proceedingID=10] Using New Data to Refine a Bayesian Network [PDF] Using New Data to Refine a Bayesian Network [bib]
A Response to "Believing on the Basis of the Evidence". Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1994. Computational Intelligence, 10:21-25.
A Response to A Response to
Learning Bayesian Belief Networks: An Approach Based on the MDL Principle. Lam, W., and Bacchus, F. 1994. Computational Intelligence, 10:269-294.
Learning Bayesian Belief Networks: An Approach Based on the MDL Principle [http://dx.doi.org/10.1111/j.1467-8640.1994.tb00166.x] Learning Bayesian Belief Networks: An Approach Based on the MDL Principle [bib]
Forming Beliefs about a Changing World. Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1994. In Proceedings of the 12th AAAI Conference on Artificial Intelligence (AAAI-1994), 222-229.
Forming Beliefs about a Changing World [PDF] Forming Beliefs about a Changing World [bib]
Generating New Beliefs from Old. Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1994. In Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1994), 37-45.
Generating New Beliefs from Old [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=485&proceedingID=10] Generating New Beliefs from Old [PDF] Generating New Beliefs from Old [bib]
Generating Degrees of Belief from Statistical Information: An Overview. Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1993. In Proceedings of the 13th Conference n Foundations of Software Technology and Theoretical Computer Science, 318-325.
Generating Degrees of Belief from Statistical Information: An Overview [PDF] Generating Degrees of Belief from Statistical Information: An Overview [bib]
Using Causal Information and Local Measures to Learn Bayesian Networks. Lam, W., and Bacchus, F. 1993. In Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1993), 243-250.
Using Causal Information and Local Measures to Learn Bayesian Networks [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=586&proceedingID=9] Using Causal Information and Local Measures to Learn Bayesian Networks [PDF] Using Causal Information and Local Measures to Learn Bayesian Networks [bib]
Using First-Order Probability Logic for the Construction of Bayesian Networks. Bacchus, F. 1993. In Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1993), 219-226.
Using First-Order Probability Logic for the Construction of Bayesian Networks [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=583&proceedingID=9] Using First-Order Probability Logic for the Construction of Bayesian Networks [PDF] Using First-Order Probability Logic for the Construction of Bayesian Networks [bib]
Statistical Foundations for Default Reasoning. Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1993. In Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI-2007), 563-569.
Statistical Foundations for Default Reasoning [PDF] Statistical Foundations for Default Reasoning [bib]
The Expected Value of Hierarchical Problem-Solving. Bacchus, F., and Yang, Q. 1992. In Proceedings of the 11th AAAI Conference on Artificial Intelligence (AAAI-1991), 369-374.
The Expected Value of Hierarchical Problem-Solving [PDF] The Expected Value of Hierarchical Problem-Solving [bib]
From Statistics to Beliefs. Bacchus, F.; Grove, A. J.; Koller, D.; and Halpern, J. Y. 1992. In Proceedings of the 11th AAAI Conference on Artificial Intelligence (AAAI-1991), 602-608.
From Statistics to Beliefs [PDF] From Statistics to Beliefs [bib]
Learning Bayesian Belief Networks. Lam, W., and Bacchus, F. 1992. In Proceedings of the Pacific Rim Conference on Atificial Intelligence (PRICAI-92), 1237-1243.
Learning Bayesian Belief Networks [PDF] Learning Bayesian Belief Networks [bib]
The Downward Refinement Property. Bacchus, F., and Yang, Q. 1991. In Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI-1991), 286-293.
The Downward Refinement Property [PDF] The Downward Refinement Property [bib]
Default Reasoning From Statistics. Bacchus, F. 1991. In Proceedings of the 10th AAAI Conference on Artificial Intelligence (AAAI-1991), 392-398.
Default Reasoning From Statistics [PDF] Default Reasoning From Statistics [bib]
A Non-Reified Temporal Logic. Bacchus, F.; Tenenberg, J. D.; and Koomen, J. A. G. M. 1991. Artif. Intell., 52(1):87-108.
A Non-Reified Temporal Logic [PDF] A Non-Reified Temporal Logic [bib]
Lp---a logic for representing and reasoning with statistical knowledge. Bacchus, F. 1990. Computational Intelligence, 6:209-231.
Lp---a logic for representing and reasoning with statistical knowledge [http://dx.doi.org/10.1111/j.1467-8640.1990.tb00296.x] Lp---a logic for representing and reasoning with statistical knowledge [bib]
Representing and Reasoning with Probabilistic Knowledge. Bacchus, F. 1990. MIT Press.
Representing and Reasoning with Probabilistic Knowledge [http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3477] Representing and Reasoning with Probabilistic Knowledge [bib] Buy
Probability and logic: a reply to Cheeseman. Bacchus, F. 1990. Computational Intelligence, 6:180-183.
Probability and logic: a reply to Cheeseman [http://dx.doi.org/10.1111/j.1467-8640.1990.tb00136.x] Probability and logic: a reply to Cheeseman [bib]
Against Conditionalization. Bacchus, F.; Jr., H. E. K.; and Thalos, M. 1990. Synthese, 85:475-506.
Against Conditionalization [http://dx.doi.org/10.1007/BF00484837] Against Conditionalization [bib]
Probabilistic Belief Logics. Bacchus, F. 1990. In Proceedings of the 9th Eureopean Conference on Artificial Intelligence (ECAI-1990), 59-64.
Probabilistic Belief Logics [PDF] Probabilistic Belief Logics [bib]
Lp: A Logic for Statistical Information. Bacchus, F. 1989. In Proceedings of the 5th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1989), 3-14.
Lp: A Logic for Statistical Information [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=771&proceedingID=5] Lp: A Logic for Statistical Information [PDF] Lp: A Logic for Statistical Information [bib]
A Non-Reified Temporal Logic. Bacchus, F.; Tenenberg, J. D.; and Koomen, J. A. G. M. 1989. In Proceedings of the 1st International Conference on Principles of Knowledge Representation and Reasoning (KR-1989), 2-10.
A Non-Reified Temporal Logic [PDF] A Non-Reified Temporal Logic [bib]
A Modest but Semantically Well Founded Inheritance Reasoner. Bacchus, F. 1989. In Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI-1989), 1104-1109.
A Modest but Semantically Well Founded Inheritance Reasoner [PDF] A Modest but Semantically Well Founded Inheritance Reasoner [bib]
Statistically Founded Degrees of Belief. Bacchus, F. 1988. In Proceedings Biennial conference on Artificial Intelligence sponsored by the Canadian Society for Computational Studies of Intelligence (CSCSI-1988), 56-66.
Statistically Founded Degrees of Belief [bib]
On probability distributions over possible worlds. Bacchus, F. 1988. In Proceedings of the 4th Annual Conference on Uncertainty in Artificial Intelligence (UAI-1988), 217-226.
On probability distributions over possible worlds [http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=823&proceedingID=4] On probability distributions over possible worlds [PDF] On probability distributions over possible worlds [bib]
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