Towards Modelling of Reactive, Goal-Oriented and Hybrid Intelligent Agents Using P Systems

  • Petros Kefalas
  • Ioanna Stamatopoulou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6501)


Intelligent agents are classified into various types depending on whether they just react to the stimuli they perceive (reactive) or they develop plans to solve their own goals (proactive or goal-oriented). In practice, agents are a mixture of two layers since they perform reactive or proactive tasks depending on what is the most appropriate at a given time (hybrid agents). Bearing in mind the dynamic organisation of a multi-agent system consisting of any of the above types, it is only natural to consider Population P Systems as a suitable candidate for modelling. In this paper, we describe preliminary work done towards modelling of MAS which include all types of agents. An initial attempt is made to tackle certain issues that have to do with the objects and rules that define each agent operation. Alongside the alternative solutions, we present a concrete example to demonstrate our findings and raise discussions.


Intelligent Agent Incoming Message Proactive Behaviour Reactive Agent Rescue Unit 
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.


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  1. 1.
    Bernardini, F., Gheorghe, M.: Population P Systems. Journal of Universal Computer Science 10(5), 509–539 (2004)MathSciNetGoogle Scholar
  2. 2.
    Coakley, S.: Formal Software Architecture for Agent-Based Modelling in Biology. PhD thesis, Dept. of Comp. Science, Univ. of Sheffield, UK (2007)Google Scholar
  3. 3.
    Georgeff, M.P., Lansky, A.L.: Reactive reasoning and planning. In: Proc. of the 6th Conference on Artificial Intelligence, pp. 677–682 (1987)Google Scholar
  4. 4.
    Kefalas, P., Holcombe, M., Eleftherakis, G., Gheorghe, M.: A formal method for the development of agent-based systems. In: Plekhanova, V. (ed.) Intelligent Agent Software Engineering, pp. 68–98. Idea Publishing Group Co., USA (2003)CrossRefGoogle Scholar
  5. 5.
    Kefalas, P., Stamatopoulou, I.: Modelling of multi-agent systems: Experiences with membrane computing and future challenges. In: Applications of Membrane computing, Concurrency and Agent-based modelling in POPulation biology (AMCA-POP), Satellite event of the 11th Conference on Membrane Computing ( to appear, 2010)Google Scholar
  6. 6.
    Kelemen, J., Kelemenova, A., Paun, G.: Preview of P colonies: A biochemically inspired computing model. In: Pollack, J.B., Bedau, M., Husbands, P., Ikegami, T., Watson, R.A. (eds.) Proceedings of the 9th Intern. Conference on the Simulation and Synthesis of Living Systems (Alife IX), pp. 82–86. MIT Press, Cambridge (2004)Google Scholar
  7. 7.
    Martin-Vide, C., Păun, G., Pazos, J., Rodriguez-Paton, A.: Tissue P systems. Theoretical Computer Science 296, 295–326 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Rao, A.S., Georgeff, M.P.: Modeling rational agents within a BDI-architecture. In: Allen, J., Fikes, R., Sandewall, E. (eds.) Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR 1991), pp. 473–484. Morgan Kaufmann, San Francisco (1991)Google Scholar
  9. 9.
    Sakellariou, I., Kefalas, P., Stamatopoulou, I.: Enhancing NetLogo to simulate BDI communicating agents. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 263–275. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Stamatopoulou, I., Gheorghe, M., Kefalas, P.: Modelling dynamic configuration of biology-inspired multi-agent systems with Communicating X-machines and Population P Systems. In: Mauri, G., Păun, G., Jesús Pérez-Jímenez, M., Rozenberg, G., Salomaa, A. (eds.) WMC 2004. LNCS, vol. 3365, pp. 389–401. Springer, Heidelberg (2005a)CrossRefGoogle Scholar
  11. 11.
    Stamatopoulou, I., Kefalas, P., Eleftherakis, G., Gheorghe, M.: A modelling language and tool for Population P Systems. In: PCI 2005 (2005b)Google Scholar
  12. 12.
    Stamatopoulou, I., Sakellariou, I., Kefalas, P., Eleftherakis, G.: OPERAS for social insects: Formal modelling and prototype simulation. Special Issue of Romanian Journal of Information Science and Technology (ROMJIST) on Natural Computing — from biology to computer science and back to applications 11(3), 267–280 (2008)Google Scholar
  13. 13.
    Wilensky, U.: Netlogo Center for Connected Learning and Computer-based Modelling. Northwestern University, Evanston, IL (1999),
  14. 14.
    Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Petros Kefalas
    • 1
  • Ioanna Stamatopoulou
    • 1
  1. 1.Department of Computer Science, CITY CollegeInternational Faculty of the University of SheffieldThessalonikiGreece

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