Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T07:17:28.843Z Has data issue: false hasContentIssue false

New trends in constraint satisfaction, planning, and scheduling: a survey

Published online by Cambridge University Press:  01 September 2010

Roman Barták*
Affiliation:
Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 2/25, 118 00 Praha 1, Czech Republic; e-mail: [email protected]
Miguel A. Salido*
Affiliation:
Instituto de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de vera s/n 46020, Valencia, Spain; e-mail: [email protected]
Francesca Rossi*
Affiliation:
Dipartimento di Matematica Pura ed Applicata, Universitá di Padova, Via Trieste 63, 35121 Padova, Italy; e-mail: [email protected]

Abstract

During recent years, the development of new techniques for constraint satisfaction, planning, and scheduling has received increased attention, and substantial effort has been invested in trying to exploit such techniques to find solutions to real-life problems. In this paper, we present a survey on constraint satisfaction, planning, and scheduling from the Artificial Intelligence point of view. In particular, we present the main definitions and techniques, and discuss possible ways of integrating such techniques. We also analyze the role of constraint satisfaction in planning and scheduling, and hint at some open research issues related to planning, scheduling, and constraint satisfaction.

Type
Articles
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, J., Balas, E., Zawack, D. 1988. The shifting bottleneck procedure for job shop scheduling. Management Science 34, 391401.CrossRefGoogle Scholar
Allen, J. F. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26(1), 832843.CrossRefGoogle Scholar
Apt, K. R. 2003. Principles of Constraint Programming. Cambridge University Press.CrossRefGoogle Scholar
Bacchus, F., van Beek, P. 1998. On the conversion between non-binary and binary constraint satisfaction problems. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98), AAAI Press, Menlo Park, 311318.Google Scholar
Baptiste, P., Le Pape, C. 1996. Edge-finding constraint propagation algorithms for disjunctive and cumulative scheduling. In Proceedings of the Fifteenth Workshop of the U.K. Planning Special Interest Group (PLANSIG), Liverpool.Google Scholar
Baptiste, P., Le Pape, C., Nuijten, W. 1995. Constraint-based optimization and approximation for job-shop scheduling. In Proceedings of the AAAI-SIGMAN Workshop on Intelligent Manufacturing Systems, IJCAI-95, Montréal, Canada.Google Scholar
Baptiste, P., Le Pape, C., Nuijten, W. 2001. Constraint-based Scheduling: Applying Constraint Programming to Scheduling. Kluwer Academic Publishers.CrossRefGoogle Scholar
Baptiste, P., Laborie, P., Le Pape, C., Nuijten, W. 2006. Constraint-based scheduling and planning. In Handbook of Constraint Programming, Francesca, R., Peter van, B. & Toby, W. (eds). Elsevier, 761799.CrossRefGoogle Scholar
Barták, R. 1998. On-line Guide to Constraint Programming. http://kti.mff.cuni.cz/~bartak/constraints/index.htmlGoogle Scholar
Barták, R. 2005. Constraint satisfaction for planning and scheduling. In Intelligent Techniques for Planning. Vrakas, D. & Vlahavas, I. (eds.). IGI Global, 320353.CrossRefGoogle Scholar
Barták, R. 2007. Generating implied boolean constraints via singleton consistency. In Abstraction, Reformulation, and Approximation (SARA 2007), Miguel, I. & Ruml, W. (eds.). Lecture Notes in Artificial Intelligence 4612, 5064, Springer-Verlag.CrossRefGoogle Scholar
Barták, R., McCluskey, L. 2007. Knowledge engineering tools and techniques for automated planning and scheduling systems. The Knowledge Engineering Review—Special Issue 22(2), 115116.CrossRefGoogle Scholar
Barták, R., Toropila, D. 2008. Reformulating constraint models for classical planning. In Proceedings of the 21st International Florida AI Research Society Conference (FLAIRS 2008), AAAI Press, Menlo Park, 525530.Google Scholar
Barták, R., Little, J., Manzano, O., Sheahan, C. 2007. From enterprise models to scheduling models: bridging the gap. In Planning, Scheduling and Constraint Satisfaction, Salido, M. A. & Fdez-Olivares, J. (eds). In CAEPIA Workshop on Planning, Scheduling and Constraint Satisfaction, Salamanca, Spain, 4456.Google Scholar
Bistarelli, S. 2004. Semirings for Soft Constraint Solving and Programming. Lecture Notes Computer Science 2962, Springer-Verlag.CrossRefGoogle Scholar
Bistarelli, S., Codognet, P., Rossi, F. 2002. Abstracting soft constraints: framework, properties, examples. AI Journal 139(2), 175211.Google Scholar
Bistarelli, S., Pini, M. S., Rossi, F., Venable, K. B. 2006. Bipolar preference problems: framework, properties and solving techniques. Lecture Notes in Computer Science 4651, Pages 78–92 of CSCLP, Springer.CrossRefGoogle Scholar
Bitner, J. R., Reingold, E. M. 1975. Backtracking programming techniques. Communications of the ACM 18, 651655.CrossRefGoogle Scholar
Blum, A., Furst, M. 1997. Fast planning through planning graph analysis. Artificial Intelligence 90, 281300.CrossRefGoogle Scholar
Borrajo, D., Veloso, M. 1996. Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review Journal. Special Issue on Lazy Learning, 134.Google Scholar
Boutilier, C., Brafman, R. I., Domshlak, C., Hoos, H. H., Poole, D. 2004. CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. JAIR 21, 135191.CrossRefGoogle Scholar
Brucker, P. 2001. Scheduling Algorithms. Spriger-Verlag.CrossRefGoogle Scholar
Carchrae, T., Beck, J. C. 2005. Applying machine learning to low knowledge control of optimization algorithms. Computational Intelligence 21, 372387.CrossRefGoogle Scholar
Carlier, J., Pinson, E. 1994. Adjustment of heads and tails for the job-shop problem. European Journal of Operational Research 78(2), 146161.CrossRefGoogle Scholar
Chen, L., Pu, P. 2004. Survey of Preference Elicitation Methods. Technical report IC/200467. Swiss Federal Institute of Technology in Lausanne (EPFL).Google Scholar
Cousot, P., Cousot, R. 1977. Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In Conference Record of the Sixth Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, ACM Press, Los Angeles, California, New York, 238–252.Google Scholar
Dechter, R. 2003. Constraint Processing. Morgan Kaufmann.Google Scholar
Dechter, R., Pearl, J. 1988. Network-based Heuristics for constraint satisfaction problems. Artificial Intelligence 34, 138.CrossRefGoogle Scholar
Dechter, R., Meiri, I., Pearl, J. 1991. Temporal constraint network. Artificial Intelligence 49, 6195.CrossRefGoogle Scholar
Ezzahir, R., Bessiere, C., Belaissaoui, M., Bouyakhf, El.-H. 2007. DisChoco: a platform for distributed constraint programming. In Proceedings of IJCAI-07 Workshop on Distributed Constraint Reasoning, Hyderabad, India.Google Scholar
Faltings, B., Yokoo, M. 2005. Introduction: special issue on distributed constraint satisfaction. Artificial Intelligence 161, 15.CrossRefGoogle Scholar
Fikes, R., Nilsson, N. 1971. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3–4), 189208.CrossRefGoogle Scholar
Freuder, E. 1982. A sufficient condition for backtrack-free search. Journal of the ACM 29, 2432.CrossRefGoogle Scholar
Freuder, E. C., Likitvivatanavong, C., Manuela, M., Rossi, F., Wallace, R. J. 2003. Computing explanations and implications in preference-based configurators. In Proceedings of CSCLP, Lecture Notes in Computer Science 2627, 76–92, Springer.CrossRefGoogle Scholar
Frost, D., Dechter, R. 1994. Dead-end driven learning. In Proceedings of the National Conference on Artificial Intelligence, AAAI Press, Menlo Park, 294300.Google Scholar
Gaschnig, J. 1977. A general backtrack algorithm that eliminates most redundant tests. In Procceedings of the International Joint Conference on Artifical Intelligence (IJCAI). Cambridge, MA, USA.Google Scholar
Gaschnig, J. 1979. Performance measurement and analysis of certain search algorithms. Technical report CMU-CS-79-124, Carnegie-Mellon University.Google Scholar
Gelain, M., Pini, M. S., Rossi, F., Venable, K. B. 2007. Dealing with incomplete preferences in soft constraint problems. Lecture Notes in Computer Science 4741, Springer, Pages 286–300 of CP.CrossRefGoogle Scholar
Ghallab, M., Nau, D., Traverso, P. 2004. Automated Planning: Theory and Practice. Morgan Kaufmann.Google Scholar
Golomb, S. W., Baumert, L. D. 1965. Backtrack programming. Communications of the ACM 12(4), 516524.Google Scholar
Graham, R. L., Lawler, E. L., Lenstra, J. K., Rinnooy-Kan, A. H. G. 1979. Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of Discrete Mathematics 5, 287326.CrossRefGoogle Scholar
Haralick, R., Elliot, G. 1980. Increasing tree efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263314.CrossRefGoogle Scholar
Hatamlou, A. R., Meybodi, M. R. 2007. A hybrid search algorithm for solving constraint satisfaction problems. In Proceedings of World Academy of Science, Engineering and Techniology 25, 362364.Google Scholar
Helmert, M. 2006. The fast downward planning system. Journal of Artificial Intelligence Research 26, 191246.CrossRefGoogle Scholar
Hoffmann, J., Nebel, B. 2001. The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253302.CrossRefGoogle Scholar
Jackson, J. R. 1955. Scheduling a Production Line to Minimize Maximum Tardiness. Research report 43. Management Science Research Project, University of California.Google Scholar
Kautz, H., Selman, B. 1992. Planning as satisfiability. In Proceedings of ECAI, IOS Press, Amsterdam, 359–363.Google Scholar
Khatib, L., Morris, P. H., Morris, R. A., Rossi, F. 2001. Temporal constraint reasoning with preferences, Pages 322–327 of IJCAI, Morgan Kaufmann.Google Scholar
Kumar, V. 1992. Algorithms for constraint satisfaction problems: a survey. AI Magazine 13, 3244.Google Scholar
Laborie, P. 2003. Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results. Artificial Intelligence 143, 151188.CrossRefGoogle Scholar
Leung, J. Y. T. 2004. Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman & Hall.CrossRefGoogle Scholar
Lhomme, O. 1993. Consistency techniques for numeric CSPs. In Proceedings of 13th International Joint Conference on Artificial Intelligence, Chambéry, France, 232–238.Google Scholar
Lopez, A., Bacchus, F. 2003. Generalizing GraphPlan by formulating planning as a CSP. In Proceedings of IJCAI, Acapulco, Mexico, 954960.Google Scholar
Marriott, K., Stuckey, P. J. 1998. Programming with Constraints: An Introduction. MIT Press.CrossRefGoogle Scholar
McCluskey, T. L., Liu, D., Simpson, R. M. 2003. GIPO II: HTN planning in a tool-supported knowledge engineering environment. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2003). AAAI Press, 90101.Google Scholar
McGann, C., Py, F., Rajan, K., Ryan, J., Henthorn, R. 2008. Adaptive control for autonomous underwater vehicles. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008), AAAI Press, Menlo Park, 13191324.Google Scholar
Michalewicz, Z., Fogel, D. B. 2000. How to Solve It: Modern Heuristics. Springer-Verlag.CrossRefGoogle Scholar
Muscettola, N., Nayak, P., Pell, B., Williams, B. 1998. Remote agent: to boldly go where no AI system has gone before. Artificial Intelligence 103, 547.CrossRefGoogle Scholar
Nareyek, A., Freuder, E. C., Fourer, R., Giunchiglia, E., Goldman, R. P., Kautz, H., Rintanen, J., Tate, A. 2005. Constraints and AI planning. IEEE Intelligent Systems 20(2), 6272.CrossRefGoogle Scholar
Peintner, B., Pollack, M. E. 2004. Low-cost Addition of Preferences to DTPs and TCSPs. Pages 723–728 of Proceedings of AAAI’-04. AAAI Press/The MIT Press.Google Scholar
Penberthy, J., Weld, D. S. 1992. UCPOP: a sound, complete, partial order planner for ADL. In Proceedings of the International Conference on Knowledge Representation and Reasoning (KR), Cambridge, Massachusetts, USA, 103114.Google Scholar
Pinedo, M. 2002. Scheduling: Theory, Algorithms, and Systems. Prentice Hall.Google Scholar
Prosser, P. 1993. Hybrid algorithm for the constraint satisfaction problem. Computational Intelligence 9, 268299.CrossRefGoogle Scholar
Puget, J. F. 2005. Automatic detection of variable and value symmetries. In Principles and Praktice of Constraint Programming (CP 2005). Lecture Notes Computer Science 3709, 475489, Springer-Verlag.CrossRefGoogle Scholar
Reiter, R. 2001. Knowledge in Action: Logical Foundation for Specifying and Implementing Dynamic Systems. MIT Press.CrossRefGoogle Scholar
Rossi, F., Sperduti, A. 1998. Learning solution preferences in constraint problems. Journal of Experimental and Theoretical Artificial Intelligence 10(1), 103116.CrossRefGoogle Scholar
Rossi, F., Van Beek, P., Walsh, T. 2006. Handbook of Constraint Programming. Elsevier.Google Scholar
Ruml, W., Do, M. B., Fromherz, M. 2005. On-line planning and scheduling for high-speed manufacturing. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2005), AAAI Press, Menlo Park, 3039.Google Scholar
Ruttkay, Z. 1998. Constraint satisfaction—a survey. CWI Quarterly 11(2&3), 123162.Google Scholar
Sacerdoti, E. 1990. The nonlinear nature of plans. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Tbilisi, Georgia, 206214.Google Scholar
Sadeh, N., Fox, M. S. 2003. Hybrid solving for CSP. ALP newsletter 16.Google Scholar
Salido, M. A. 2007. Distributed CSPs: why it is assumed a variable per agent? In Abstraction, Reformulation, and Approximation, 7th International Symposium (SARA 2007), Lecture Notes in Artificial Intelligence 4612, 407–408, Springer-Verlag, Whistler, Canada.CrossRefGoogle Scholar
Salido, M. A., Barber, F. 2006. Distributed CSPs by graph partitioning. Applied Mathematics and Computation 183, 491498.CrossRefGoogle Scholar
Smith, S. F., Cheng, Ch.-Ch. 1993. Slack-based heuristics for constraint satisfaction scheduling. In Proceedings of the National Conference on Artificial Intelligence (AAAI), AAAI Presss, Menlo Park, 139144.Google Scholar
Tate, A. 1977. Generating project networks. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Cambridge, Massachusetts, USA, 888893.Google Scholar
Torres, P., Lopez, P. 2000. On not-first/not-last conditions in disjunctive scheduling. European Journal of Operational Research 127, 332343.CrossRefGoogle Scholar
Tsang, E. 1993. Foundation of Constraint Satisfaction. Academic Press.Google Scholar
van Beek, P., Chen, X. 1999. CPlan: a constraint programming approach to planning. In Proceedings of AAAI-99, AAAI Press, Menlo Park, 585–590.Google Scholar
van Hentenryck, P. 1989. Constraint Satisfaction in Logic Programming. MIT Press.Google Scholar
Vidal, T., Fargier, H. 1999. Handling contigency in temporal constraint networks. Journal of Experimental and Theoretical Artificial Intelligence Research 11(1), 2345.CrossRefGoogle Scholar
Vidal, V., Geffner, H. 2004. Branching and pruning an optimal temporal POCL planner based on constraint programming. In Proceedings of AAAI-04, AAAI Press, Menlo Park, 570577.Google Scholar
Vilím, P. 2004. O(n log n) filtering algorithms for unary resource constraint. In Proceedings of CP-AI-OR, Nice, France.CrossRefGoogle Scholar
Vilím, P., Barták, R., Cepek, O. 2005. Extension of O(n log n) filtering algorithms for the unary resource constraint to optional activities. Constraints 10(4), 403425.CrossRefGoogle Scholar
Vu, X. H., O’Sullivan, B. 2007. Semiring-based constraint acquisition. In Proceedings of ICTAI 2007, IEEE Computer Society, Patras, Greece, 251–258.Google Scholar
Waltz, D. L. 1972. Generating Semantic Description from Drawings of Scenes with Shadows. Technical report AI-TR-271, MIT, Cambridge.Google Scholar
Waltz, D. L. 1975. Understanding line drawings of scenes with shadows. The Psychology of Computer Vision, 1991.Google Scholar
Wolf, A. 2003. Pruning while sweeping over task intervals. In Principles and Practice of Constraint Programming (CP 2003), Lecture Notes in Computer Science 2833, 739753, Springer.CrossRefGoogle Scholar
Yokoo, M., Durfee, E. H., Ishida, T., Kuwabara, K. 1992. Distributed constraint satisfaction for formalizing distributed problem solving. In 12th International Conference on Distributed Computing Systems (ICDCS-92), Yokohama, Japan, 614621.Google Scholar