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A.E. Croker, V. Dhar, "A Knowledge Representation for Constraint Satisfaction Problems," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 5, pp. 740752, October, 1993.  
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@article{ 10.1109/69.243506, author = {A.E. Croker and V. Dhar}, title = {A Knowledge Representation for Constraint Satisfaction Problems}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {5}, number = {5}, issn = {10414347}, year = {1993}, pages = {740752}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.243506}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  A Knowledge Representation for Constraint Satisfaction Problems IS  5 SN  10414347 SP740 EP752 EPD  740752 A1  A.E. Croker, A1  V. Dhar, PY  1993 KW  knowledge representation; constraint satisfaction problems; constraintdriven reasoner; Boolean expressions; dependency network; control information; preference orderings; constraint handling; inference mechanisms; knowledge representation VL  5 JA  IEEE Transactions on Knowledge and Data Engineering ER   
We present a general representation for problems that can be reduced to constraint satisfaction problems (CSP) and a model for reasoning about their solution. The novel part of the model is a constraintdriven reasoner that manages a set of constraints specified in terms of arbitrarily complex Boolean expressions and represented in the form of a dependency network. This dependency network incorporates control information (derived from the syntax of the constraints) that is used for constraint propagation, contains dependency information that can be used for explanation and for dependencydirected backtracking, and is incremental in the sense that if the problem specification is modified, a new solution can be derived by modifying the existing solution. The constraintdriven reasoner is coupled to a problem solver which contains information about the problem variables and preference orderings.
[1] C. Alexander,Notes on the Synthesis of Form. Cambridge, MA: Harvard University Press, 1964.
[2] V. Chvatal, "Edmonds polytopes and a hierarchy of combinatorial problems,"Discrete Mathematics, vol. 4, 1973.
[3] A. Croker, V. Dhar, and D. McAllester, "Dependency directed backtracking for generalized satisficing assignment problems," to appear inManagement Science. Available asTech. Report 190, Department of Information Systems, NYU, 1988.
[4] H. Crowder, E. Johnson, and M. Padberg, "Solving largescale zeroone linear programming problems,"Operations Research, vol. 31, Sept.Oct. 1983.
[5] G. Dantzig,Linear Programming and Extensions. Princeton, NJ: Princeton University Press, 1963.
[6] R. Dechter and J. Pearl, "Networkbased heuristics for constraintsatisfaction problems,"Artificial Intell., vol. 34, pp. 137, 1988.
[7] R. Dechter, "Methodolgy for CSP's," Workshop on Constraint Processing, IJCAI, Detroit, MI, Aug. 1989.
[8] V. Dhar, and H. E. Pople, "Rulebased versus structurebased models for explaining and generating expert behavior,"Commun. ACM, vol. 30, pp. 542555, 1987.
[9] V. Dhar and P. Ranganathan, "Experiments with an integer programming formulation of an expert system,"MCC Tech. Rep. ACAAI02289, Austin, TX, Feb. 1989.
[10] J. Doyle, "A truth maintenance system,"Artificial Intelligence, June 1979.
[11] M. S. Fox, N. Sadeh, and C. Baykan, "Constrained heuristic search," inProc. Eleventh Int. Joint Conf. Artificial Intelligence, Detroit, MI, pp. 309315, Aug. 1989.
[12] E. C. Freuder, "Synthesizing constraint expressions,"Comm. ACM, vol. 21, no. 11, pp. 958966, 1978.
[13] R.E. Gomory, "Outline of an algorithm for integer solutions to linear programs, " in R.L. Graves and P. Wolfe, Eds.,Recent Advances in Mathematical Programming. New York: McGrawHill, 1963.
[14] J.W. Goodwin, "A process theory of nonmonotonic inference,"Proc. Ninth Int. Joint Conf. Artificial Intelligence, 1985.
[15] M. Grotschel and M. Padberg,The Travelling Salesman Problem: A Guided Tour of Combinatorial Optimization. New York: Wiley, 1982.
[16] P. Hansen, "Methods of nonlinear 01 programming,"Annals of Discrete Mathematicsvol. 5, pp. 5370, 1974.
[17] R.M. Haralick and G.L. Elliot, "Increasing tree search efficiency for constraint satisfaction,"Artificial Intelligence, vol. 14, pp. 263313, Aug. 1980.
[18] G.E Hinton, "Relaxation and its role in vision," Ph.D dissertation,University of Edinburgh, 1977.
[19] J. N. Hooker, "A quantitative approach to logical inference,"Decision Support Syst., vol 4, no 1, 1988.
[20] N. Karmarker, "A New PolynomialTime Algorithm for Linear Programming,"Combinatorica, Vol. 4, No. 4, 1984, pp. 373395.
[21] A. Mackworth, "Consistency in networks of relations,"Artificial Intelligence, vol. 8, pp. 99118, 1977.
[22] D. McAllester, "Reasoning utility package,"AI Laboratory Memo 667, Apr. 1982.
[23] U. Montanari, "Networks of constraints: Fundamental properties and application to picture processing,"Information Science, vol. 7, 1974.
[24] U. Montanari and F. Rossi, "Constraint relaxation may be perfect,"Artificial Intelligence, to be published.
[25] B. Nudel, "Consistent labeling problems and their algorithms: Expectedcomplexities and theorybased heuristics,"Artificial Intelligence, vol. 21, pp. 135178, 1983.
[26] B. Nudel, "Solving the general consistent labeling problem: Two algorithms and their expected complexities," inProc. National Conf. Artificial Intelligence, Aug. 1983.
[27] C. Petrie, D. Russinoff, and D. Steiner, "Proteus 2: System description,"MCC Tech. Rep. AI13687, May 1987.
[28] M. Reinfrank, "Lecture notes on reason maintenance systems," Tech.Rep. INF2 ARM588, Siemens AG, 1988.
[29] W. R. Reitman,Cognition and Thought. New York: Wiley, 1965.
[30] H. Simon, "The structure of illstructured problems,"Artificial Intelligence, vol. 4, Sept. 1973.
[31] D. Waltz, "Understanding line drawings of scenes with shadows," in P.H. Winston, Ed.,ThePsychology of Computer Vision. New York: McGrawHill, 1975, pp. 1991.
[32] L.J. Watters, "Reduction of integer polynomial programming problems to zeroone linear programming problems,"Operations Research, vol. 15, 1967, pp. 11711174.