Cognitive Algorithms and Systems of Error Monitoring, Conflict Resolution and Decision Making

  • Pedro U. LimaEmail author
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)


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.


Markov Decision Process Discrete Event System Plan Representation Input Place Primitive Action 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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work have been published before and are cited throughout the chapter, most of the results in that section were still unpublished at the time of writing this text.


  1. Akharware N (2000) Pipe2: Platform Independent Petri Net Editor, MSc Thesis, Imperial College of Science, Technology and Medicine, University of London, London, UKGoogle Scholar
  2. 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)Google Scholar
  3. Cassandras C, Lafortune S (2007) Introduction to Discrete Event Systems, SpringerGoogle Scholar
  4. Cohen PR, Levesque HJ (1991) Teamwork, Special Issue on Cognitive Science and Artificial Intelligence, 25(4):486–512Google Scholar
  5. 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, USAGoogle Scholar
  6. 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, PortugalGoogle Scholar
  7. 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)Google Scholar
  8. Girault C, Valk R (2003) Petri Nets for Systems Engineering: A Guide to Modeling, Verification, and Applications, SpringerGoogle Scholar
  9. 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, CanadaGoogle Scholar
  10. 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)Google Scholar
  11. Kotb YT, Beauchemin SS, Barron JL (2007) Petri Net-Based Cooperation in Multi-Agent Systems, 4th Canadian Conference on Computer and Robot VisionGoogle Scholar
  12. 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, USAGoogle Scholar
  13. Milutinovic D, Lima PU (2002) Petri Net Models of Robotic Tasks, Proceedings of IEEE International Conference on Robotics and Automation, Washington DC, USAGoogle Scholar
  14. 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):59–76CrossRefGoogle Scholar
  15. Saridis GN (1979) Toward Realization of Intelligent Control, Proceedings IEEE, 27Google Scholar
  16. Sutton R, Barto A (1998) Reinforcement Learning, The MITGoogle Scholar
  17. Viswanadham N, Narahari Y (1992) Performance Modeling of Automated Manufacturing Systems, Prentice HallGoogle Scholar
  18. 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):257–271CrossRefGoogle Scholar
  19. Watkins CJCH, Dayan P (1992) Q-learning, Machine Learning, 8, 279–292Google Scholar
  20. 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, USAGoogle Scholar
  21. Ziparo V, Iocchi L (2006) Petri Net Plans, Proceedings of the 4th International Workshop on Modelling of Objects, Components, and Agents (MOCA06), Turku, FinlandGoogle Scholar
  22. 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, PortugalGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Institute for Systems and Robotics, Instituto Superior TécnicoLisboaPortugal

Personalised recommendations