Introduction
In the previous Chapter, we have presented several blind search or uninformed search techniques. Uninformed search methods systematically explore the search space until the goal is reached. As evident, uninformed search methods pursue options that many times lead away from the goal. Even for some small problems the search can take unacceptable amounts of time and/or space. The blind search techniques lack knowledge about the problem to solve and this makes them inefficient in many cases. Using problem specific knowledge can significantly improve the search speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading (1984)
Nilsson, N.J.: Principles of Artificial Intelligence. Tioga Publishing Company (1980)
Stentz, A.: Optimal and Efficient Path Planning for Partially-Known Environments. In: Proceedings IEEE International Conference on Robotics and Automation, pp. 3310–3317 (1994)
Stentz, A.: Optimal and Efficient Path Planning for Unknown and Dynamic Environments, Carnegie Mellon Robotics Institute Technical Report CMU-RI-TR-93-20 (August 1993)
Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of A*. Journal of the ACM 32(3), 505–536 (1985)
Mahanti, A., Ghosh, S., Nau, D.S., Pal, A.K., Kanal, L.: Performance of IDA* on trees and graphs. In: 10th Nat. Conf. on Art. Int., AAAI 1992, San Jose, CA, pp. 539–544 (1992)
Reinefeld, A.: Complete solution of the Eight-Puzzle and the benefit of node-ordering in IDA*. In: Procs. Int. Joint Conf. on AI, Chambéry, Savoi, France, pp. 248–253 (1993)
Chakrabarti, P., Ghosh, S., Acharya, A., DeSarkar, S.: Heuristic search in restricted memory. Artificial Intelligence 47, 197–221 (1989)
Ikeda, T., Imai, H.: Enhanced A* algorithms for multiple alignments: Optimal alignments for several sequences and k-opt approximate alignments for large cases. Theoretical ComputerScience 210, 341–374 (1999)
Gaschnig, J.: Performance measurement and analysis of certain search algorithms. Technical Report CMU-CS-79-124, Computer Science Department, Carnegie Mellon University (1979)
Hansson, O., Mayer, A., Heuristic, A.: search as evidential reasoning. In: Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence. Morgan Kaufmann, Windsor (1989)
Kaindl, H., Khorsand, A.: Memory-bounded bidirectional search. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI 1994), Seattle, Washington, pp. 1359–1364. AAAI Press, Menlo Park (1994)
Kanal, L.N., Kumar, V.: Search in Artificial Intelligence. Springer, Berlin (1988)
Kumar, V., Kanal, L.N.: The CDP: A unifying formulation for heuristic search, dynamic programming, and branch-and-bound. In: Kanal, L.N., Kumar, V. (eds.) Search in Artificial Intelligence, ch. 1, pp. 1–27. Springer, Berlin (1988)
Mostow, J., Prieditis, A.E.: Discovering admissible heuristics by abstracting and optimizing: a transformational approach. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI 1989), vol. 1, pp. 701–707. Morgan Kaufmann, Detroit (1989)
Newell, A., Ernst, G.: The search for generality. In: Kalenich, W. (ed.) Information Processing Proceedings of IFIP Congress 1965, vol. 1, pp. 17–24 (1965)
Nilsson, N.J.: Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, New York (1971)
Korf, R.E.: Depth-first iterative-deepening: an optimal admissible tree search. Artificial Intelligence 27(1), 97–109 (1985)
Korf, R.E.: Iterative-deepening A*: An optimal admissible tree search. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI 1985), pp. 1036–1043. Morgan Kaufmann, Los Angeles (1985)
Korf, R.E.: Optimal path finding algorithms. In: Kanal, L.N., Kumar, V. (eds.) Search in Artificial Intelligence, ch. 7, pp. 223–267. Springer, Berlin (1988)
Korf, R.E.: Linear-space best-first search. Artificial Intelligence 62(1), 41–78 (1993)
Pohl, I.: Bi-directional and heuristic search in path problems. Technical Report 104, SLAC (Stanford Linear Accelerator Center), Stanford, California (1969)
Pohl, I.: First results on the effect of error in heuristic search. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence 5, pp. 219–236 (1970)
Pohl, I.: The avoidance of (relative) catastrophe, heuristic competence, genuine dynamic weighting and computational issues in heuristic problem solving. In: Proceedings of the Third International Joint Conference on Artificial Intelligence (IJCAI 1973), Stanford, California, pp. 20–23 (1973)
Pohl, I.: Practical and theoretical considerations in heuristic search algorithms. In: Elcock, E.W., Michie, D. (eds.) Machine Intelligence 8, pp. 55–72 (1977)
Russell, S.J.: Efficient memory-bounded search methods. In: Proceedings of the 10th European Conference on Artificial Intelligence (ECAI 1992), Vienna, Austria, pp. 1–5 (1992)
Zhou, R., Hansen, E.A.: Memory-Bounded A* Graph Search. In: Proceedings of 15th International FLAIRS Conference, Pensecola, Florida (2002)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Pearson Education, London (2003)
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Grosan, C., Abraham, A. (2011). Informed (Heuristic) Search. In: Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21004-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-21004-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21003-7
Online ISBN: 978-3-642-21004-4
eBook Packages: EngineeringEngineering (R0)