Abstract
We present an algebraic model for search problems and their solutions. The model includes in its formalism dynamic programming, branch and bound, and other implicit enumeration techniques. Search problems are defined over a finite set of conjuncts, a nonassociative analogue of strings. The generalization to conjuncts is important because it allows the model to handle naturally problems with objects (e.g., binary trees) that combine in a nonassociative manner. The solution of a search problem requires the identification of a minimal element of the set of feasible conjuncts. We model the solution process in terms of two types of operators. Each of these operators enumerates a set of conjuncts, and in so doing infers the indentity of the only conjunct in the set that can possibly be extended to a minimal feasible conjunct. The operators themselves are based on computationally feasible dominance relations that order certain pairs of conjuncts, and whose axioms allow us to infer the orderings of the feasible extensions of such conjuncts.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bellman, R. E. and Dreyfus, S. E., Applied Dynamic Programming, Princeton University Press, Princeton, N.J., 1062.
Borodin, A., Fischer, M.J., Kirkpatrick, D.G., Lynch, N.A., and Tompa, M., A time-space tradeoff for sorting and related non-oblivious computations, Proc. 20th Symposium on Foundations of Computer Science, (1079) 312–318.
Brown, Q. E., Dynamic programming in computer science, Technical Report - Computer Science Department (1070), Carnegie-Mellon University.
Gnesi, S., Martelli, A., and Montanari, U., Dynamic programming as graph searching, J. ACM 28(1982), 737 - 751.
Heiman, P., A new theory of dynamic programming, Ph.D dissertation, University of Michigan, Ann Arbor, 1982.
Heiman, P., The principle of optimality in the design of efficient algorithms, J. Math. Anal. Appl. 119(1086), 97 - 127.
Heiman, P., A common schema for dynamic programming and branch and bound type algorithms, submitted to J. ACM (also available as Technical Report CS86–4 - Computer Science Department (1986), University of New Mexico).
Helman, P. and Rosenthal, A., A comprehensive model of dynamic programming, SIAM J. on Algebraic and Discrete Methods 6(1985), 310–334.
Horowitz, E. and Sahni, S., “Fundamentals of Computer Algorithms,” Computer Science Press, Potomac, Maryland, 1978.
Ibaraki, T., Solvable classes of discrete dynamic programming, J. Math. Anal. Appl. 43(1073), 642 - 603.
Ibaraki, T., On the optimality of algorithms for finite state sequential decision processes, J. Math. Anal. Appl. 53(1076), 618 - 643.
Ibaraki, T., The power of dominance relations in branch and bound algorithms, J. ACM 24(1077), 264 - 270.
Ibaraki, T., Branch-and-bound procedure and state-space representation of combinatorial optimization problems, Inform. Control 36(1078), 1 - 27.
Karp, R. M. and Held, M., Finite-state processes and dynamic programming, SIAM J. Appl. Math. 15(1967), 693 - 718.
Kumar, V., A unified approach to problem solving search procedures, Ph.D dissertation, University of Maryland, College Park, 1082.
Kumar, V. and Kanal, L., A general branch and bound formulation for understanding and synthesizing and/or tree search procedures, Artificial Intelligence 21(1983), 170 - 198.
Kumar, V. and Kanal, L., The composite decision process: A unifying formulation for heuristic search, dynamic programming and branch bound procedures, Proceedings of The 1083 Conference on Artificial Intelligence, 220–224.
Moore, E.F. and Gilbert, E.N., Variable length encodings, Bell System Tech. J. 38(1050), 033 - 068.
Purdom, P. W., Jr., and Brown, C. A., The analysis of algorithms, Holt, Rinehart and Winston, New York, New York, 1085.
Rosenthal, A., Dynamic programming is optimal for nonserial optimization problems, SIAM J. Comput 11(1082), 47 - 50.
Ullman, J. D., Principles of Database Systems, Computer Science Press, Potomac, Maryland, 1082.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1988 Springer-Verlag New York Inc.
About this chapter
Cite this chapter
Helman, P. (1988). An Algebra for Search Problems and Their Solutions. In: Kanal, L., Kumar, V. (eds) Search in Artificial Intelligence. Symbolic Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8788-6_2
Download citation
DOI: https://doi.org/10.1007/978-1-4613-8788-6_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4613-8790-9
Online ISBN: 978-1-4613-8788-6
eBook Packages: Springer Book Archive