Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios

Original Article


In this paper an agent-based simulation is developed in order to evaluate an Ambient Intelligence scenario based on agents. Many AmI applications are implemented through agents but they are not compared with any other existing alternative in order to evaluate the relative benefits of using them. The proposed simulation environment analyses such benefits using two evaluation criteria: First, measuring agent satisfaction of different types of desires along the execution. Second, measuring time savings obtained through a correct use of context information. In this paper an existing agent architecture, an ontology and a 12-steps protocol to provide AmI services in airports, is evaluated using the NetLogo simulation environment. In our NetLogo model we are considering scalability issues of this application domain but using FIPA and BDI extensions to be coherent with our previous works and our previous JADE implementation of them. The NetLogo model simulates an airport with agent ‘passengers’ passing through several zones located in a specific order in a map: passport controls, check-in counters of airline companies, boarding gates, different types of shopping. Although the initial data in each simulation is generated randomly, and the model is just an approximation of real-world airports, the definition of this case of use of AmI through NetLogo agents opens an interesting way to evaluate the benefits of using AmI, which is a significant contribution to the final development of AmI systems.


agents Ambient Intelligence context-aware ubiquitous techniques software simulations 


  1. Aarts E, Harwig E and Schuurmans M (2001). Ambient intelligence. In: Denning J (ed) The Invisible Future. McGraw-Hill: New York.Google Scholar
  2. Afsarmanesh H, Guevara M and Hertzberger L (2004). Virtual community support in telecare. In: Camarinha-Matos L and Afsarmanesh H (eds). Processes and Foundations for Virtual Organizations, Springer, pp 211–220.Google Scholar
  3. Bajo J et al (2008). An execution time planner for the ARTIS agent architecture. Journal of Engineering Applications of Artificial Intelligence 21 (5): 769–784.CrossRefGoogle Scholar
  4. Barton JJ and Vijayaraghavan V (2002). Ubiwise, a ubiquitous wireless infrastructure simulation environment. HP LABS.Google Scholar
  5. Bellifemine F, Poggi A and Rimassa G (2000). Developing Multi-Agent Systems with JADE. In: Intelligent Agents VII Agent Theories Architectures and Languages, Springer, pp 89–103.Google Scholar
  6. Bordini RH and Hübner JF (2005). BDI agent programming in agentspeak using Jason. In: Computational Logic in Multi-agent Systems, Springer, pp 143–164.Google Scholar
  7. Brooks RA (1986). A robust layered control system for a mobile robot. IEEE Journal on Robotics and Automation 2(1): 14–23.Google Scholar
  8. Caballero A, Bota J and Gómez-Skarmeta A (2011). Using cognitive agents in social simulations. Engineering Applications of Artificial Intelligent 24 (7): 1098–1109.CrossRefGoogle Scholar
  9. Corchado JM, Bajo J, de Paz Y and Tapia DI (2008). Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decision Support Systems 44 (2): 382–396.CrossRefGoogle Scholar
  10. Dawson RJ, Peppe R and Wang M (2011). An agent based model for risk-based flood incident management. Natural Hazards 59 (1): 167–189.CrossRefGoogle Scholar
  11. Durfee EH and Rosenschein JS (1994). Distributed problem solving and multi-agent systems: Comparisons and examples. Ann Arbor 1001 (48109): 29.Google Scholar
  12. Fuentes V, Carbó J and Molina JM (2006). Heterogeneous domain ontology for location based information system in a multi-agent framework. IDEAL, Springer, pp 1199–1206.Google Scholar
  13. Grninger M and Fox MS (1995). Methodology for the design and evaluation of ontologies. In: International Joint Conference on Artificial Inteligence (IJCAI95), Workshop on Basic Ontological Issues in Knowledge Sharing. ACM, Morgan Kaufmann Publishers Inc., pp 1–10.Google Scholar
  14. Huget M-P (1997). Foundations for Intelligent Physical Agents Specification. Ginebra: Suiza.Google Scholar
  15. Kovács GL and Kopácsi S (2006). Some aspects of ambient intelligence. Acta Polytechnica Hungarica 3 (1): 35–60.Google Scholar
  16. Lech TC and Wienhofen LWM (2005). AmbieAgents: A scalable infrastructure for mobile and context-aware information services. In: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. ACM, pp 625–631.Google Scholar
  17. Nieto-Carvajal I, Bota JA, Ruiz PM and Gómez-Skarmeta AF (2004). Implementation and evaluation of a location-aware wireless multi-agent system. In: Embedded and Ubiquitous Computing. Springer, pp 528–537.CrossRefGoogle Scholar
  18. Nishikawa H et al (2006). UbiREAL: Realistic Smartspace Simulator for Systematic Testing. In: UbiComp 2006: Ubiquitous Computing. Springer, pp 459–476.Google Scholar
  19. O’Hare GMP, O’Grady MJ, Kegan S, O’Kane D, Tynan R and Marsh D (2004). Inteligent agile agents: Active enablers for ambient intelligence. In: ACM’s Special Interest Group on Computer-Human Interaction (SIGCHI), Ambient Intelligence for Scientific Discovery (AISD) Workshop. Vienna.Google Scholar
  20. O’Neill E, Klepal M, Lewis D, O’Donnell T, O’Sullivan D and Pesch D (2005). A testbed for evaluating human interaction with ubiquitous computing environments. In: First International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities. IEEE, pp 60–69.Google Scholar
  21. Pan X, Han CS, Dauber K and Law KH (2007). A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI Soc. 22 (2): 113–132.CrossRefGoogle Scholar
  22. Pavón J, Gómez-Sanz J, Fernández-Caballero A and Valencia-Jiménez JJ (2007). Development of intelligent multisensor surveillance systems with agents. Robotics and Autonomous Systems 55 (12): 892–903.CrossRefGoogle Scholar
  23. Pokahr A, Braubach L and Lamersdorf W (2003). Jadex: Implementing a bdi-infrastructure for jade agents. EXP—In Search of Innovation (Special Issue on JADE) 3 (3): 76–85.Google Scholar
  24. Polhill JG (2015). Extracting owl ontologies from agent-based models: A netlogo extension. Journal of Artificial Societies and Social Simulation 18 (2): 15.CrossRefGoogle Scholar
  25. Poslad S, Laamanen H, Malaka R, Nick A, Buckle P and Zipf A (2001). Crumpet: Creation of user-friendly mobile services personalized for tourism. In: 3G Mobile Communication Technologies, 2001. Second International Conference on (Conf. Publ. No. 477). IET, pp 28–32.Google Scholar
  26. Rao AS and Georgeff MP (1995). BDI agents: From theory to practice. In: ICMAS, Vol. 95, pp 312–319.Google Scholar
  27. Sakellariou I, Kefalas P and Stamatopoulou I (2008). Enhancing netlogo to simulate bdi communicating agents. In: Darzentas J, Vouros GA, Vosinakis S and Arnellos A (eds). Artificial Intelligence: Theories, Models and Applications, (Lecture Notes in Computer Science) Vol. 5138, Springer: Berlin Heidelberg, pp 263–275.CrossRefGoogle Scholar
  28. Sanchez-Pi N, Carbo J and Molina JM (2008). Jade/leap agents in an aml domain. In: Hybrid Artificial Intelligence Systems. Springer, pp 62–69.CrossRefGoogle Scholar
  29. Sanchez-Pi N, Carbo J and Molina JM (2010). Analysis and design of a multi-agent system using Gaia methodology in an airport case of use. Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial 14 (45): 9–17.Google Scholar
  30. Schilit B, Adams N and Want R (1994). Context-aware computing applications. In: Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, Washington DC: IEEE Computer Society, pp 85–90 (WMCSA ‘94).Google Scholar
  31. Serrano E, Poveda G and Garijo M (2014). Towards a holistic framework for the evaluation of emergency plans in indoor environments. Sensors 14 (3): 4513–4535.CrossRefGoogle Scholar
  32. Sichman JS, Conte R and Gilbert N (eds). (1998). Multi-agent systems and agent-based simulation: First International Workshop, MABS’98, Paris, France, July 4–6, Proceedings. Springer: Berlin, Heidelberg, pp 94–104.Google Scholar
  33. Sánchez-Pi N, Fuentes V, Carbó J and Molina JM (2007). Knowledge-based system to define context in commercial applications. In: 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2007. IEEE: China, Tsingtao, pp 694–699.Google Scholar
  34. Wagner N and Agrawal V (2014). An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster. Expert Systems with Applications 41 (6): 2807–2815.CrossRefGoogle Scholar
  35. Weiser M. (1991). The computer of the 21st century. Scientific American 265 (3): 66–75.CrossRefGoogle Scholar
  36. Wooldridge M and Jennings NR (1994). Agent theories, architectures and languages: A survey. In: Intelligent Agents. Springer, pp 1–39.Google Scholar

Copyright information

© Operational Research Society 2016

Authors and Affiliations

  1. 1.Universidad Carlos III de MadridMadridSpain
  2. 2.Universidade do Estado do Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations