Abstract
There are currently several approaches to decision making in complex systems, particularly in robotics. In most cases, the decision-making process resembles the well-known control or sense–think–act loop: the process output or state is sensed, its deviation (error) from the desired value is continuously monitored and, based on some appropriate algorithm, a control action is picked from the available action set to be applied to the process, so that the loop is closed and the decision-making process moves to its next iteration.
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
References
Akharware N (2000) Pipe2: Platform Independent Petri Net Editor, MSc Thesis, Imperial College of Science, Technology and Medicine, University of London, London, UK
Albanese M, Chellappa R, Moscato V, Picariello A, Subrahmanian VS, Turaga P, Udrea O (2008) A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video, IEEE Transactions On Multimedia, 10(6)
Cassandras C, Lafortune S (2007) Introduction to Discrete Event Systems, Springer
Cohen PR, Levesque HJ (1991) Teamwork, Special Issue on Cognitive Science and Artificial Intelligence, 25(4):486–512
Costelha H, Lima PU (2007) Modelling, Analysis and Execution of Robotic Tasks using Petri Nets, Proceedings of IEEE International Conference on Intelligent Robots and Systems, San Diego, CA, USA
Costelha H, Lima PU (2008) Modelling, Analysis and Execution of Multi-Robot Tasks using Petri Nets, Proceedings of 7th International Joint Conference on Autonomous Agents and Multi-Agent Systems, Estoril, Portugal
Giordano V, Ballal P, Lewis F, Turchiano B, Zhang JB (2006) Supervisory Control of Mobile Sensor Networks: Math Formulation, Simulation, and Implementation, IEEE Transactions on Systems, Man and Cybernetics — Part B:Cybernetics, 36(4)
Girault C, Valk R (2003) Petri Nets for Systems Engineering: A Guide to Modeling, Verification, and Applications, Springer
Kim G, Chung W, Park S-K, Kim M (2005) Experimental Research of Navigation Behaviour Selection Using Generalized Stochastic Petri Nets (GSPN) for a Tour-Guide Robot, Proceedings of IEEE International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada
King J, Pretty RK, Gosine RG (2003) Coordinated Execution of Tasks in a Multiagent Environment, IEEE Transactions on Systems, Man and Cybernetics — Part A: Systems and Humans, 33(5)
Kotb YT, Beauchemin SS, Barron JL (2007) Petri Net-Based Cooperation in Multi-Agent Systems, 4th Canadian Conference on Computer and Robot Vision
Lima PU, Grácio H, Veiga V, Karlsson A (1998) Petri Nets for Modelling and Coordination of Robotic Tasks, Proceedings of 1998 IEEE International Conference on Systems, Man and Cybernetics, San Diego, USA
Milutinovic D, Lima PU (2002) Petri Net Models of Robotic Tasks, Proceedings of IEEE International Conference on Robotics and Automation, Washington DC, USA
Montano L, García FJ, Villarroel JL (2000) Using the Time Petri Net Formalism for Specification, Validation, and Code Generation in Robot-Control Applications, International Journal of Robotics Research, 19(1):59–76
Saridis GN (1979) Toward Realization of Intelligent Control, Proceedings IEEE, 27
Sutton R, Barto A (1998) Reinforcement Learning, The MIT
Viswanadham N, Narahari Y (1992) Performance Modeling of Automated Manufacturing Systems, Prentice Hall
Wang F-Y, Kyriakopoulos K, Tsolkas A, Saridis GN (1993) A Petri-Net Coordination Model for an Intelligent Mobile Robot, IEEE Transactions on Robotics and Automation, 9(3):257–271
Watkins CJCH, Dayan P (1992) Q-learning, Machine Learning, 8, 279–292
Zimmermann A, Freiheit J (1998) TimeNETMS-an Integrated Modeling and Performance Evaluation Tool for Manufacturing Systems, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, San Diego, CA, USA
Ziparo V, Iocchi L (2006) Petri Net Plans, Proceedings of the 4th International Workshop on Modelling of Objects, Components, and Agents (MOCA06), Turku, Finland
Ziparo V, Ziparo A, Iocchi L, Nardi D, Palamara PF, Costelha H (2008) Petri Net Plans, A Formal Model for Representation and Execution of Multi-Robot Plans. Proc. of 7th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2008), Padgham, Parkes, Müller and Parsons (eds.), May 12–16., 2008, Estoril, Portugal
Acknowledgements
The simulations whose results are presented in Sect. 15.3 were carried out by the PhD student Mr. Hugo Costelha. While some parts of his PhD thesis work have been published before and are cited throughout the chapter, most of the results in that section were still unpublished at the time of writing this text.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Lima, P.U. (2011). Cognitive Algorithms and Systems of Error Monitoring, Conflict Resolution and Decision Making. In: Cutsuridis, V., Hussain, A., Taylor, J. (eds) Perception-Action Cycle. Springer Series in Cognitive and Neural Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1452-1_15
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1452-1_15
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-1451-4
Online ISBN: 978-1-4419-1452-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)