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Computing Science 665
Seminar in Artificial Intelligence
Constraint-Based Programming
Fall 1998
Detailed Outline:
Week 1 (Monday, September 7):
Reminder: Monday, September 7 is the Labor Day Holiday
Topic: Overview and preliminaries
 NP-Completeness and combinatorial problems
 Constraint-based problem solving
 Standard modeling and resolution methods
 Constraint satisfaction problem framework
 Motivating applications: scheduling, temporal reasoning, propositional satisfiability
Readings:
 T.H. Cormen, C.E. Leiserson, R.L. Rivest, Introduction to Algorithms. Chapters 1-5 & 36.
 J.D. Ullman, Principles of Database and Knowledge-Base Systems, Vol. 1, pages 43-65.
Week 2 (Monday, September 14):
Topic: Local consistency, constraint propagation
 Arc-consistency, path-consistency, k-consistency, global-consistency, directionalconsistency
Readings:
 A. K. Mackworth. Consistency in networks of relations. Artif. Intell., 8:99-118, 1977.
 C. Bessière. Arc-consistency and arc-consistency again. Artif. Intell., 65:179-190, 1994
 P. van Beek and R. Dechter. Constraint tightness and looseness versus local and global
consistency. J. ACM, 44:549-566, 1997.
Discussion leader(s):
Week 3 (Monday, September 21):
Topic: Modeling
 Solution quality, soft constraints, feasibility and optimality
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Alternative models that correctly capture conceptual constraints
Abstractions of constraints, built-in constraints, linear constraints
Readings:
 B. Smith, S. C. Brailsford, P. M. Hubbard, and H. P. Williams. The progressive party
problem: Integer linear programming and constraint programming compared. Constraints,
1:119-138, 1996.
 B. A. Nadel. Representation selection for constraint satisfaction: a case study using nqueens. IEEE Expert, 5:16-23, 1990.
 A. Aggoun, and N. Beldiceanu. Extending CHIP in order to solve complex scheduling and
placement problems. Mathl. Comput. Modelling, 17:57-73, 1993.
 J.-C. Regin. A filtering algorithm for constraints of difference in CSP. Proc. of AAAI-94,
Discussion leader(s):
Week 4 (Monday, September 28):
Topic: Modeling (continued)
 Improving the efficiency of a model: adding redundant variables, adding redundant
constraints, translating to a different representation
Readings:
 B. Smith. A template design problem. 1998.
 R. Dechter and I. Meiri. Experimental evaluation of preprocessing techniques in constraint
satisfaction problems. Artif. Intell., 68:211-242, 1994.
 F. Bacchus and P. van Beek, Proc. of AAAI-98.
Discussion leader(s):
Week 5 (Monday, October 5):
Topic: Solving
 Search spaces
 Backtracking search algorithms: forward checking, backmarking, backjumping, constraintrecording (learning)
Readings:
 M. Bruynooghe. Backtracking. Encyclopedia of AI.
 R. M. Haralick and G. L. Elliott. Increasing tree search efficiency for constraint satisfaction
problems. Artif. Intell., 14:263-313, 1980.
 P. Prosser. Hybrid algorithms for the constraints satisfaction problem. Computational
Intelligence, 9:268-299, 1993.
 G. Kondrak and P. van Beek. A theoretical evaluation of selected backtracking algorithms.
Proc. of the 14th International Joint Conference on Artificial Intelligence, pages 451-547,
Montreal, Quebec, 1995.
Discussion leader(s):
Week 6 (Monday, October 12):
Reminder: Monday, October 12 is the Thanksgiving Day Holiday
Topic: Solving (continued)
 Heuristics for backtracking algorithms: variable and value ordering heuristics
 Case study: Davis-Putnam algorithms for propositional satisfiability
Readings:
 P. Gent, E. MacIntyre, P. Prosser, B. M. Smith, and T. Walsh. An empirical study of
dynamic variable ordering heuristics for the constraint satisfaction problem. Proc. of the
2nd International Conference on Principles and Practice of Constraint Programming,
Cambridge, Mass., 1996.
 J. W. Freeman. Hard random 3-SAT problems and the Davis-Putnam procedure. Artif.
Intell., 81:183-198, 1996.
 R. J. Bayardo Jr. and R. Schrag. Using CSP look-back techniques to solve real-world SAT
instances. Proc. 14th International Conference on Artificial Intelligence, pages 203-208,
Providence, Rhode Island, 1997.
Discussion leader(s):
Week 7 (Monday, October 19):
Topic: Solving (continued)
 Branch and bound search algorithms
Readings:
 E. C. Freuder and R. J. Wallace. Partial constraint satisfaction. Artif. Intell., 58:21-70,
1992.
 G. Verfaillie, M. Lemaitre, and T. Schiex. Russian doll search for solving constraint
optimization problems. Proc. of AAAI-96.
Discussion leader(s):
Week 8 (Monday, October 26):
Topic: Solving (continued)
 Stochastic or local search algorithms: GSAT, hill-climbing
Readings:
 A. Davenport, E. Tsang, C. J. Wang, and K. Zhu. GENET: A connectionist architecture for
solving constraint satisfaction problems by iterative improvement. Proc. of the 12th National
Conference on Artificial Intelligence, pages 325-330, Washington, DC, 1994.
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S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Solving large-scale constraint
satisfaction and scheduling problems using a heuristic repair method. Proc. of the 8th
National Conference on Artificial Intelligence, pages 17-24, Boston, Mass., 1990.
B. Selman, and H. A. Kautz. An empirical study of greedy local search for satisfiability
testing. Proc. of the 11th National Conference on Artificial Intelligence, pages 46-52,
Washington, DC, 1993.
Discussion leader(s):
Week 9 (Monday, November 2):
Reminder: Wednesday, November 11 is the Remembrance Day Holiday
Topic: Solving (continued)
 Stochastic or local search algorithms: simulated annealing, genetic algorithms, taboo search
Readings:
 S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing,
Science, 220:671-680, 1983.
 A. Hertz, E. Taillard, D. de Werra. Tabu Search. In Aarts. E. and Lenstra, J. K. (Eds.),
Local search in combinatorial optimization, pages 121-136. Wiley-Interscience.
 E. P. K. Tsang and T. Warwick. Applying genetic algorithms to constraint satisfaction
optimization problems. Proc. of ECAI’90.
Discussion leader(s):
Week 10 (Monday, November 9):
Topic: Solving (continued)
 Synthesis algorithms or dynamic programming algorithms, adaptive-consistency
Readings:
 R. J. Bayardo Jr. and D. P. Miranker. On the space-time trade-off in solving constraint
satisfaction problems. In Proc. of the 14th International Joint Conference on Artificial
Intelligence, pages 558-562, Montreal, 1995.
 E. C. Freuder. Synthesizing constraint expressions. Comm. ACM, 21:958-966, 1978.
 R. Seidel. A new method for solving constraint satisfaction problems. In Proc. of the 7th
International Joint Conference on Artificial Intelligence, pages 338-342, Vancouver, 1981.
Discussion leader(s):
Week 11 (Monday, November 16):
Topic: Evaluating solutions and solution techniques
 Easy and hard problems, random models of problems, robustness
Readings:
 P. Cheeseman, B. Kanefsky, and W. M. Taylor. Where the really hard problems are. In
Proc. of the 12th International Joint Conference on Artificial Intelligence, pages 331-337,
Sydney, Australia, 1991.
 D. Mitchell, B. Selman, and H. Levesque. Hard and easy distributions of SAT problems. In
Proc. of the 10th National Conference on Artificial Intelligence, pages 459-465, San Jose,
Calif., 1992.
Discussion leader(s):
Week 12 (Monday, November 23):
Topic: Constraint programming languages and systems
 Research systems
 Commercial systems
Readings:
 J.-F. Puget . A C++ implementation of CLP. Proc. of SPICIS 94. Singapore, 1994.
 http://www.cirl.uoregon.edu/constraints/systems/index.html