Reading list
All papers in this reading list are available on the Web (linked below) or in the course reader available at the Stanford Bookstore.
- Mitchell, D., Selman, B., and Levesque, H. (1992). Hard and Easy Distributions of SAT Problems. In Procs. of AAAI-92, 459-465.
- Selman, B., Levesque, H., and Mitchell, D. (1992). A New Method for Solving Hard Satisfiability Problems. In Procs. of AAAI-92, 440-446.
- Gent, I., and Walsh, T. (1993). Towards an Understanding of Hill-climbing Procedures for SAT. In Procs. of AAAI-93, 28-33.
- Selman, B., Kautz, H., and Cohen, B. (1994). Noise Strategies for Improving Local Search. In Procs. of AAAI-94, 337-343.
- Li, C. and Anbulagan. (1997). Heuristics Based on Unit Propagation for Satisfiability Problems. In Procs. of IJCAI-97.
- Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., Malik, S. (2001). Chaff: Engineering an Efficient SAT Solver. In Procs. of DAC-2001.
- Wu, Z. and Wah, B. (1999). Trap Escaping Strategies in Discrete Lagrangian Methods for Solving Hard Satisfiability and Maximum Satisfiability Problems. Procs. of AAAI-99, 673-678.
- Van Hentenryck, P., Deville, Y., and Teng, C. (1992). A Generic Arc-Consistency Algorithm and its Specializations. Artificial Intelligence 57, 291-321.
- Prosser, P. (1993). Hybrid Algorithms for the Constraint Satisfaction Problem. Computational Intelligence 9(3), 268-299.
- Bacchus, F. and van Run, P. (1995). Dynamic Variable Ordering in CSPs. Principles and Practice of Constraint Programming - CP-95, 258-275.
- Kondrak, G. and van Beek, P. (1997). A Theoretical Evaluation of Selected Backtracking Algorithms. Artificial Intelligence 89, 365-387.
- Ginsberg, M. (1993). Dynamic Backtracking. JAIR 1, 25-46.
- Bayardo, R. and Schrag, R. (1997). Using CSP Look-back Techniques to Solve Real-world SAT instances. In Procs. of AAAI-97, 203-208.
- Jegou, P. and Terrioux, C. (2003) Hybrid Backtracking Bounded by Tree-Decomposition of Constraint Networks. Artificial Intelligence 146 43-75.
- Gent, I. and Smith, B. (2003) Symmetry Breaking During Search in Constraint Programming. Procs. of ECAI-2000.
- Dechter, R., Meiri, I., and Pearl, J. (1991). Temporal Constraint Networks. Artificial Intelligence 49, 61-95.
- Smith, S. and Cheng, C. (1993). Slack-based Heuristics for Constraint Satisfaction Scheduling. In Procs. of AAAI-93, 139-144.
- Laborie, P., (2003) Algorithms for Propagating Resource Constraints in AI Planning and Scheduling: Existing Approaches and New Results. In Artificial Intelligence 143, 151-188.
- Muscettola, N. (2002). Computing the Envelope for Stepwise-Constant Resource Allocations. In Principles and Practice of Constraint Programming, 139-154.
- Blum, A. and Furst, M. (1997). Fast Planning Through Planning Graph Analysis. Artificial Intelligence 90, 281-300.
- Kautz, H. and Selman, B. (1999). Unifying SAT-based and Graph-based Planning. In Procs. of IJCAI-99.
- Bonet , B. and Geffner, H. (1999). Planning as Heuristic Search: New Results. Procs. of ECP-99, 360-372. (Springer)
- Gerevini, A. and Serina I. (2002). LPG: a planner based on local search for planning graphs. In Procs of AIPS-02, 13?22.
- Nguyen, X. and Kambhampati K. (2000). Extracting Effective and Admissible State Space Heuristics from the Planning Graph. In Procs. of AAAI-2000.
- Frank, J. and Jonsson, A (2003) Constraint-Based Attribute and Interval Planning. Constraints 8 339-364.
- 26. R-Moreno, M.D., Oddi, A, Borrajo,, D., and Cesta, A. (2006) IPSS: A Hybrid Approach to Planning and Scheduling Integration. IEEE Trans. On KDE 18 1681-1695.
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