Abstract
Planning belongs to fundamental AI domains. Examples of planning applications are manufacturing, production planning, logistics and agentics. Over the decades planning techniques were improved and now they are able to capable real environment problems in the presence of uncertain and incomplete information. This article introduces the notion of so called classical planning, indicating connected with this computational complexity problems and possible ways of treating uncertainty.
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References
Backstrom, C.: Computational Aspects of Reordering Plans. Journal of Artificial Intelligence Research 9, 99â137 (1998)
Baral, C., Kreinovich, V., Trejo, R.: Computational complexity of planning and approximate planning in presence of incompleteness. In: Proc. 16th International Joint Conference on Artificial Intelligence, IJCAI 1999 (1999)
Barret, A., Weld, D.S.: Partial-Order Planning: Evaluating Possible Efficiency Gains. Artificial Intelligence 67, 71â112 (1994)
Blythe, J.: An Overview of Planning Under Uncertainty. Pre-print from AI Magazine 20(2), 37â54 (1999)
Blythe, J.: Planning Under Uncertainty in Dynamic Domains. Ph.D. Dissertation. Carnegie Mellon University Computer Science Department
Bylander, T.: The Computational Complexity of Propositional STRIPS Planning. Artificial Intelligence 69, 165â204 (1994)
Bylander, T.: A probabilistic analysis of propositional STRIPS planning. Artificial Intelligence 81, 241â271 (1996)
Bylander, T.: A linear programming heuristic for optimal planning. In: Proceedings of the 14th National Conference on Artificial Intelligence, pp. 694â699 (1997)
Cocosco, C.A.: A review of STRIPS: A new approach to the application of theorem proving to problem solving by R.E. Fikes, N.J. Nillson, 1971. For 304-526B Artificial Intelligence 4 (1998)
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Wprowadzenie do algorytmĂłw. WNT, Warszawa (1997)
Gerevini, A., Schubert, L.: Accelerating Partial Orders Planners: Some Techniques for Effective Search Control and Pruning. Journal of Artificial Intelligence Research 5, 95â137 (1996)
Gupta, M.M.: Intelligence, uncertainty and information. In: Machine Intelligence and Pattern Recognition, Analysis and Management of Uncertainty: Theory and Applications, vol.13. North-Holland (1992)
Gupta, N., Nau, D.S.: On the complexity of Blocks - World planning. Artificial Intelligence 56(2-3), 223â254 (1992)
Haddawy, P., Suwandi, M.: Decision-Theoretic Refinement Planning using Inheritance Abstraction. In: Hammond, K. (ed.) Proc. Second International Conference on Artificial Intelligence Planning Systems. University of Chicago, AAAI Press, Illinois
Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice-Hall International, Inc., USA (1988)
Koehler, J., Hoffmann, J.: On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm. Journal of Artificial Intelligence Research 12, 339â386 (2000)
Kushmerick, N., Hanks, S., Weld, D.: An algorithm for probabilistic least-commitment planning. In: Proc. Twelfth National Conference on Artificial Intelligence, pp. 1073â1078. AAAI Press (1994)
Leckie, C., Zukerman, I.: Inductive Learning of Search Control Rules for Planning. Artificial Intelligence 101, 63â98 (1998)
Long, D., Fox, M.: Efficient Implementation of the Plan Graph in STAN. Journal of Artificial Intelligence Research 10, 87â115 (1999)
McAllester, D.: Nonlinear Strips Planning. Lecture notes for Massachusetts Institute of Technologyâs course: 6.824, Artificial Intelligence (1992)
Nillson, N.J., Fikes, R.E.: STRIPS: A new approach to the application of theorem proving to problem solving. Technical Note 43, SRI Project 8259, Artificial Intelligence Group, Stanford Research Institute (October 1970)
Nillson, N.J.: Principles of Artificial Intelligence. Toga Publishing Company, Palo Alto (1980)
Papadimitriou, C.: Computational Complexity. Addison Wesley (1994)
Poole, D., Mackworth, A., Goebel, R.: Computational Intelligence: A Logical Approach. Oxford University Press (1998)
Rintanen, J.: Constructing Conditional Plans by a Theorem-Prover. Journal of Artificial Intelligence Research 10, 323â352 (1999)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall Series in Artificial Intelligence (2003)
Stanford Research Institute: Shakey the robot, http://www.ai.sri.com/shakey/
Veloso, M., Carbonell, J., Perez, A., Borrajo, D., Fink, E., Blythe, J.: Integrating planning and learning: The prodigy architecture. Journal of Experimental and Theoretical AIÂ 7, 81â120 (1995)
Weld, D.S.: Recent Advantages in AI Planning. Technical Report UW-CSE-98-10-01; also AI Magazine (1999)
Weld, D.S., Anderson, C.R., Smith, D.E.: Extending Graphplan to Handle Uncertainty & Sensing Actions. In: Proc. 15th National Conf. on AI, pp. 897â904 (1998)
Wikipedida, a free encyclopedia, http://en.wikipedia.org (Cited July 18, 2008)
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GaĆuszka, A., Pacholczyk, M., Bereska, D., Skrzypczyk, K. (2013). Planning as Artificial Intelligence Problem - Short Introduction and Overview. In: Nawrat, A., Simek, K., Ćwierniak, A. (eds) Advanced Technologies for Intelligent Systems of National Border Security. Studies in Computational Intelligence, vol 440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31665-4_8
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DOI: https://doi.org/10.1007/978-3-642-31665-4_8
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