Skip to main content
SpringerLink
Log in

Problem Solving: Introduction to Search Methods

  • Chapter
Intelligent Systems for Engineering

  • 244 Accesses

Abstract

Human problem solving involves search. Therefore, to simulate human problem solving in a computer we need to develop algorithms for search. In this chapter we will lay a foundation for later chapters on advanced problem solving techniques by discussing basic search methods. Before a formal definition of problem solving is provided, we will discuss some concepts with a small, yet illustrative, example.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aigner, M., Combinatorial Search, John Wiley & Sons, New York, 1988.

    MATH  Google Scholar 

  2. Barr, A. and Feigenbaum, E., The Handbook of Artificial Intelligence, Volume 1. William Kaufmann Inc., Los Altos, California, pages 190–191, 1981.

    MATH  Google Scholar 

  3. Bender, E., Mathematical Methods in Artificial Intelligence, IEEE Computer Science Press, Los Alamitos, CA, 1996.

    MATH  Google Scholar 

  4. Bradley, S. P., Hax, A. C, and Magnanti, T. L., Applied Mathematical Programming, Addison Wesley Publishing Company, Reading, Massachusetts, 1977.

    Google Scholar 

  5. Brown, D. R. and Hwang, K-Y., “Solving Fixed Configuration Problems with Genetic Search,” Research in Engineering Design, Vol. 5, No. 2, 1993, pages 88–104.

    Article  Google Scholar 

  6. Cagan, J. and Mitchell, W., “Optimally Directed Shape Generation by Shape Annealing,” Environmental and Planning B, 1992.

    Google Scholar 

  7. Carbonell, J., AI Languages and Problem Solving, In The Role of Language in Problem Solving, Jerigan, R. and Hamill, B. W. (Editors), Elsevier Science Publishers, pp. 83–102, 1985.

    Google Scholar 

  8. Cerny, V., “A Thermodynamical Approach to the Travelling Salesman Problem,” Journal of Optimization, Theory and Applications, Volume 45, pp. 41–51, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  9. Chapman, C, Kazuhiro, S., and Jakela, M., Genetic Algorithms as an Approach to Configuration and Topology Design, Technical Report, Dept. of Mechanical Engineering, MIT, Cambridge, MA 02139.

    Google Scholar 

  10. Dandhe, S. G. and Gupta, A., “Computer-Aided Interactive Graphical Design of Multispeed Gearboxes,” Journal of Mechanisms, Transmissions, and Automation in Design, Vol. 106, pp. 164–171, June 1984.

    Google Scholar 

  11. Devadas, S. and Newton, A., “Topological Optimization of Multi-Array Logic,” IEEE Transactions of Computer-Aided Design of Integrated Circuits and Systems, Vol. CAD-6, pp. 915–941, 1987 (Reference reported in Jain and Agogino).

    Article  Google Scholar 

  12. Dechter, R. and Pearl, J., “Generalized Best-First Search Strategies and the Optimality of A*,” Journal of the Association for Computing Machinery, 32(3): 505–536, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  13. Gaschnig, J., Performance Measurement and Analysis of Certain Search Algorithms, Ph. D. Dissertation, Dept. of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 [also available as PITMAN publication].

    Google Scholar 

  14. Gero, J. and Sudweeks, F. (Editors), Artificial Intelligence in Design’ 96, Kluwer Academic Publishers, 1996

    Google Scholar 

  15. Ginsberg, M., Essentials of Artificial Intelligence, Morgan Kaufmann Publishers, San Mateo California, 1993.

    Google Scholar 

  16. Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, 1989.

    Google Scholar 

  17. Grefenstette, J. J., A User’s Guide to GENESIS: Version 5.0, 1990 [can be obtained by ftping to ftp.aic.nrl.navy.mil in directory bob/galist/src/ga/genesis.tar.c].

    Google Scholar 

  18. Hart, P., Nilsson, N., and Raphael, B., “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions on SSC, Vol. SSC-4, pp. 100–107, 1968.

    Google Scholar 

  19. Holland, J. H., Adaptation in Natural and Artificial Systems, Univ. Michigan Press, Ann Arbor, 1975 [MIT Press Edition]

    Google Scholar 

  20. Humair, S., Gorti, S., Sriram, R. D., and Talukdar, S., Solving Constraint Satisfaction Problems using ATeams, Technical Report, Intelligent Engineering Systems Laboratory, MIT, Cambridge, MA 02139.

    Google Scholar 

  21. Jain, P. and Agogino, A., “Optimal Design of Mechanisms Using Simulated Annealing: Theory and Applications,” Proceedings of the 14th ASME Design Automation Conference, pp. 233–240, 1988.

    Google Scholar 

  22. Kanal, L. and Kumar, V. (Editors), Search in Artificial Intelligence, Springer Verlag, 1988.

    Google Scholar 

  23. Kant, E., Notes on Task-oriented Intelligent Systems, Graduate course in Artificial Intelligence, Carnegie-Mellon University, 1983.

    Google Scholar 

  24. Kim, J. and Khosla, P., “A Multi-Population Genetic Algorithm and its Application to Design of Manipulators,” In Proceedings of the International Conference on Intelligent Robots and Systems, IEEE, 1992.

    Google Scholar 

  25. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., “Optimization by Simulated Annealing,” Science, Vol. 220, pp. 671–680, May 13, 1983.

    Article  MathSciNet  MATH  Google Scholar 

  26. Korf, R., “Search: A Survey of Recent Results,” In Exploring Artificial Intelligence, Shrobe, H. (Editor), Morgan Kaufmann, In., pp 197–237, 1988.

    Google Scholar 

  27. Koza, J., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1991.

    Google Scholar 

  28. Laird, J. and Newell, A., A Universal Weak Method, Technical Report No: CMU-CS-83-141, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA 15213, 1983.

    Google Scholar 

  29. Luger, G. and Stubblefield, W., Artificial Intelligence and the Design of Expert Systems, The Benjamin/Cummings Publishing Company, Inc., Redwood City, Calfornia, 1989.

    MATH  Google Scholar 

  30. Michalewic, Z. Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, 1992.

    Google Scholar 

  31. Papalambros, P. and Wilde, D., Principles of Optimal Design, Cambridge University Press, 1988, 1991.

    Google Scholar 

  32. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., Teller, E., “Equation of State Calculations by Fast Computing Machines,” Journal of Chemical Physics, Vol. 21, pp. 1087–1092, 1953 [as reported in [25]].

    Article  Google Scholar 

  33. Murthy, S. Synergy in Cooperating Agents: Designing Manipulators from Task Specifications, Unpublished Ph. D. thesis, submitted to the Dept. of Electrical and Computer Engineering, CMU, Pittsburgh, PA 15213, 1992.

    Google Scholar 

  34. Nahar, S., Sahni, S., and Shragowitz, E., “Experiments with Simulated Annealing,” Proceedings of the IEEE 22nd Design Automation Conference, pp. 748–752.

    Google Scholar 

  35. Newell, A. and Simon, H., Human Problem Solving, Prentice Hall Inc., Englewood Cliffs, New Jersey, 1972.

    Google Scholar 

  36. Nilsson, N., Principles of Artificial Intelligence, Tioga Publishing Company, Palo Alto, California, 1980 [Now sold by Morgan Kaufmann, Inc.].

    MATH  Google Scholar 

  37. Otten, R.H.J.M. and van Ginneken, L.P.P.P., The Annealing Algorithm, Kluwer Academic Publishers, 1989.

    Google Scholar 

  38. Pearl, J., HEURISTICS: Intelligent Search Strategies for Computer Problem Solving, Addison Wesley Publishing Company, Reading, MA, 1984.

    Google Scholar 

  39. Quadrel, R., Asynchronous Design Environments: Architecture and Behavior, Ph. D. Thesis, Department of Architecture, Carnegie Mellon University, Pittsburgh, PA 15123, 1991 [See also Quadrel, et al., Controlling Asynchronous Team Design Environments by Simulated Annealing, in Research in Engineering Design, Vol. 5, No. 2].

    Google Scholar 

  40. Rao, S. S. and Eslampour, H. R., “Multistage Multiobjective Optimization of Gearboxes,” Journal of Mechanisms, Transmissions, and Automation in Design, ASME, Vol. 108, pp. 461–468, December 1986.

    Article  Google Scholar 

  41. Rao, S. S., Optimisation: Theory and Applications, Wiley Eastern, 1985.

    Google Scholar 

  42. Reddy, G. and J. Cagan, “An Improved Shape Annealing Algorithm for Truss Topology Generation,” ASME Journal of Mechanical Design, Vol 117, No. 2A, pp 315–321, 1995.

    Article  Google Scholar 

  43. Rich, E. and Knight, K., Artificial Intelligence, McGraw Hill, Second Edition, 1991.

    Google Scholar 

  44. Schmidt, L. C. and Cagan, J., “Recursive Annealing: A Computational Model for Machine Design,” Research in Engineering Design, Vol. 7, pp 102–125, 1995.

    Article  Google Scholar 

  45. Sridhar, J. and Rajendran, C, “Scheduling in Flowshop and Cellular Manufacturing Systems with Multiple Objectives — A Genetic Algorithmic Approach,” Production Planning and Control, Vol. 7, No. 4, 374–382, Taylor and Francis, 1996.

    Article  Google Scholar 

  46. Sriram, D., Logcher, R., and Fukuda, S. (Editors), Computer-Aided Cooperative Product Development, Springer Verlag, 1991.

    Google Scholar 

  47. Szykman, S. and J. Cagan, “Synthesis of Optimal Non-Orthogonal Routes,” Advances in Design Automation, DE-Vol. 82, ASME, pp 431–438, 1995.

    Google Scholar 

  48. Szykman, S. and J. Cagan, “Constrained Three Dimensional Layout Using Simulated Annealing,” Concurrent Product Design, DE-Vol. 74, ASME, pp 175–184.

    Google Scholar 

  49. Talukdar, S. and DeSouza, P., “Scale Efficient Organizations,” In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 1992.

    Google Scholar 

  50. Thornton, A. C, “Genetic Algorithms Versus Simulated Annealing: Satisfaction of Large Sets of Algebraic Mechanical Design Constraints,” Artificial Intelligence in Design’ 94, J. S. Gero (ed.), Elsevier, pp 381–298, 1994.

    Google Scholar 

  51. Tong, S., Powell, D., and Cornett, D., “Engineous: A Unified Method for Design Automation, Optimization, and Integration,” In Artificial Intelligence in Engineering Design, Vol III, Tong, C. and Sriram, D. (Editors), pages 235–254, 1992.

    Google Scholar 

  52. van Laarhoven, P.J.M., and Aarts, E.H.L., Simulated Annealing: Theory and Applications, D. Reidel Publishing Company (A member of Kluwer Academic Publishers), 1987.

    Google Scholar 

  53. Winston, P., Artificial Intelligence, Addison-Wesley Publishing Company, Massachusetts, 1984, 1992.

    Google Scholar 

  54. Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press, PWS Publishers, 20 Park Plaza, Boston, MA 02116, 1987.

    MATH  Google Scholar 

  55. Wong, D. G., Leong, H. W., and Liu, C. L., Simulated Annealing for VLSI Design, Kluwer Academic Publishers, 1988.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Manufacturing Systems Integration Division Manufacturing Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899-0001, USA

    Dr. Ram D. Sriram

Authors
  1. Dr. Ram D. Sriram
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag London

About this chapter

Cite this chapter

Sriram, R.D. (1997). Problem Solving: Introduction to Search Methods. In: Intelligent Systems for Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-0631-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0631-9_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1167-2

  • Online ISBN: 978-1-4471-0631-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation