Abstract
Planning belongs to fundamental AI domains. Examples of planning applications are manufacturing, production planning, logistics and agentics. In real world applications knowledge about environment is incomplete, uncertain and approximate. It implies that planning in the presence of different kind of uncertainty is more complex than classical planning. Aim of this paper is to show the way of reasoning basing on the incomplete information about the initial state of planning problem. The proper reasoning about the state of the problem can reduce such understood uncertainty and then increase efficiency of planning. The article presents an algorithm created in order to reason the state of scene from block world basing on incomplete information from two cameras observing the scene from top and side. The algorithm is explained using an example. Additionally, possible types of uncertainties are presented.
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
Baral, C., Kreinovich, V., Trejo, R.: Computational complexity of planning and approximate planning in presence of incompleteness. Artificial Intelligence 122, 241–267 (2000)
Blythe, J.: An Overview of Planning Under Uncertainty. Pre-print from AI Magazine 20(2), 37–54 (1999)
Bylander, T.: The computational complexity of propositional STRIPS planning. Artificial Intelligence 69, 165–204 (1994)
Bylander, T.: A linear programming heuristic for optimal planning. In: Proceedings of the 14th National Conference on Artificial Intelligence, pp. 694–699 (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 (1998)
Galuszka, A., Swierniak, A.: Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy. Journal of Intelligent and Robotic Systems 58(3), 239–251 (2010)
Grzejszczak, T.: Semantic representation of Block World Environment: algorithm of scene reasoning from incomplete information. Electrical Review, R. 87 NR 2/2011 (2011) (to be published)
Gupta, N., Nau, D.S.: On the complexity of Blocks World planning. Artificial Intelligence 56(2-3), 223–254 (1992)
Kim, K.H., Hong, G.-P.: A heuristic rule for relocating blocks. Computers & Operations Research 33, 940–954 (2006)
Koehler, J., Schuster, K.: Elevator Control as a Planning Problem. In: The Fifth International Conference on Artificial Intelligence Planning and Scheduling Systems Breckenridge, CO, April 15-19, pp. 331–338 (2000)
Nillson N.J., R.E. Fikes: 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 (1970)
Slaney, J., Thiebaux, S.: Block World revisited. Artificial Intelligence 125, 119–153 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grzejszczak, T., Galuszka, A. (2011). On Planning in Multi-agent Environment: Algorithm of Scene Reasoning from Incomplete Information. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_77
Download citation
DOI: https://doi.org/10.1007/978-3-642-21498-1_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21497-4
Online ISBN: 978-3-642-21498-1
eBook Packages: Computer ScienceComputer Science (R0)