Using the affordance concept for model design in agent-based simulation

  • Franziska Klügl


When designing an Agent-Based Simulation Model a central challenge is to formulate the appropriate interactions between agents as well as between agents and their environment. In this contribution we present the idea of capturing agent-environment interactions based on the “affordance” concept. Originating in ecological psychology, affordances represent relations between environmental objects and potential actions that agents may perform using those objects. We assume that explicitly handling affordances based on semantic annotation of entities in simulated space may offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: firstly a methodology for using the affordance concept to identify interactions and secondly a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.


Agent-based simulation Model design Affordance theory Agent activities Theory of motivation 

Mathematics Subject Classification (2010)

68T42 68U20 68N19 


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  1. 1.
    Awaad, I., Kretschmar, G., Hertzberg, J.: Finding ways to get the job done: an affordance-based approach. In: Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014), Portsmouth, USA, June 2014, pp. 499–503 (2014)Google Scholar
  2. 2.
    Bandini, S., Federici, M.L., Vizzari, G.: Situated cellular agents approach to crowd modeling and simulation. Cybern. Syst. 38(7), 729–753 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Brom, C., Lukavsky, J., Sery, O., Poch, T., Safrata, P.: Affordances and level-of detail AI for virtual humans. In: Proceedings of Game Set and Match 2 (2006)Google Scholar
  4. 4.
    Carrascosa, C., Klügl, F., Ricci, A., Boissier, O.: From physical to virtual: widening the perspective on multi-agent environments. In: Weyns, D., Michel, F. (eds.) Agent Environments for Multi-Agent Systems IV: 4th Int. Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May, 2014, pp. 133–146 (2015)Google Scholar
  5. 5.
    Chemero, A.: An outline of a theory of affordances. Ecol. Psychol. 15(2), 181–195 (2003)CrossRefGoogle Scholar
  6. 6.
    Cornwell, J.B., O’Brien, K., Silverman, B.G., Toth, J.A.: Affordance theory for improving the rapid generation, composability, and reusability of synthetic agents and objects (2003)Google Scholar
  7. 7.
    Epstein, J.M.: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press (2007)Google Scholar
  8. 8.
    Gibson, J.J.: The Ecological Approach to Visual Perception. Houghton Mifflin (1979)Google Scholar
  9. 9.
    Heckel, F.W.P., Youngblood, G.M.: Contextual affordances for intelligent virtual characters. In: Proceedings of the IVA 2011, LNAI 6895, pp. 202–208 (2001)Google Scholar
  10. 10.
    Hermans, T., Rehg, J.M., Bobick, A.F.: Decoupling behavior, perception, and control for autonomous learning of affordances. In: IEEE International Conference on Robotics and Automation (ICRA), 2013, pp. 4989–4996 (2013)Google Scholar
  11. 11.
    Horton, T.E., Chakraborty, A., Amant, R.S.: Affordances for robots: a brief survey. AVANT (2012)Google Scholar
  12. 12.
    Jonietz, D., Timpf, S.: An affordance-based simulation framework for assessing spatial suitability. In: Proceedings of COSIT 2013, Spatial Information Theory (LNCS 8116), pp. 169–184 (2013)Google Scholar
  13. 13.
    Joo, J., Kim, N., Wysk, R.A., Rothrock, L., Son, Y.J., Oh, Y.G., Lee, S.: Agent-based simulation of affordance-based human behaviors in emergency evacuation. Simul. Model. Pract. Theory 13, 99–115 (2013)CrossRefGoogle Scholar
  14. 14.
    Jordan, T., Raubal, M., Gartrell, B., Egenhöfer, M.J.: An affordance-based model of place in gis. In: Poiker, T., Chrisman, N. (eds.) Proceedings of 8th International Symposium on Spatial Data Handling, pp 98–109. CA, Vancouver (1998)Google Scholar
  15. 15.
    Kapadia, M., Singh, S., Hewlett, W., Faloutsos, P.: Egocentric affordance fields in pedestrian steering. In: Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games (I3d ’09), pp 215–223. ACM, New York, NY, USA (2009)Google Scholar
  16. 16.
    Klügl, F.: Affordance-based interaction design for agent-based simulation models. In: Bulling, N. (ed.) Multi-Agent Systems: 12th Eur. Conference, EUMAS 2014, Prague, Czech Republic, Dec. 2014, Revised Selected Papers, pp 51–66. Springer, Cham (2015)Google Scholar
  17. 17.
    Klügl, F., Bazzan, A.L.C.: Agent-based modeling and simulation. AI Mag. 33(3), 29–40 (2012)Google Scholar
  18. 18.
    Klügl, F., Davidsson, P.: AMASON: abstract meta-model for agent-based simulation. In: Proceedings of MATES 2013, pp. 101–141. Springer, Berlin (LNAI8076) (2013)Google Scholar
  19. 19.
    Klügl, F., Oechslein, C., Puppe, F., Dornhaus, A.: Multi-agent modelling in comparison to standard modelling. Simulation News Europe 40, 3–9 (2004)Google Scholar
  20. 20.
    Koppula, H.S., Saxena, A.: Anticipating human activities using object affordances for reactive robotic response. In: Robotics: Science and Systems IX, Berlin, June 2013 (2013)Google Scholar
  21. 21.
    Ksontini, F., Mandiau, R., Guessoum, Z., Espié, S.: Affordance-based agent model for traffic simulation. Journal of Autonomous Agents and Multiagent Systems online first (2014)Google Scholar
  22. 22.
    Kubera, Y., Mathieu, P., Picault, S.: IODA: An interaction-oriented approach for multi-agent based simulations. Auton. Agent. Multi-Agent Syst. 23(3), 303–343 (2011)CrossRefGoogle Scholar
  23. 23.
    Locatelli, M.P., Vizzari, G.: Awareness in collaborative ubiquitous environments: the multilayered multi-agent situated system approach. ACM Transactions on Autonomous and Adaptive Systems 2, 13.1–13.21 (2007)CrossRefGoogle Scholar
  24. 24.
    Lopez y Lopez, F., Luck, M., d’Inverno, M.: A normative framework for agent-based systems. Computational and Mathematical Organization Theory 12(2–3), 227–250 (2006)CrossRefGoogle Scholar
  25. 25.
    Maslow, A.H.: A theory of human motivation. Psychol. Rev. 50(4), 370–396 (1943)CrossRefGoogle Scholar
  26. 26.
    Montesano, L., Lopes, M., Bernardino, A., Santos-Victor, J.: Learning object affordances: from sensory–motor coordination to imitation. IEEE Trans. Robot. 24(1), 15–26 (2008)CrossRefGoogle Scholar
  27. 27.
    Murphy, R.R.: Case studies of applying Gibson’s ecological approach to mobile robotics. IEEE Trans. Syst. Man Cybern. Part A Syst. Humans 29(1), 105–111 (1999)CrossRefGoogle Scholar
  28. 28.
    Norman, D.A.: The Invisible Computer. MIT Press (1999)Google Scholar
  29. 29.
    van Oijen, J., Vanhee, L., Dignum, F.: CIGA: a middleware for intelligent agents in virtual environments. In: Proceedings AEGS 2011, LNAI 7471, pp. 22–37. Springer (2012)Google Scholar
  30. 30.
    Ortmann, J., Kuhn, W.: Affordances as qualities. In: Galton, A., Mizoguchi, R. (eds.) Proceedings of the 2010 Conference on Formal Ontology in Information Systems (FOIS 2010), pp 117-130. IOS Press, The Netherlands (2010)Google Scholar
  31. 31.
    Papasimeon, M.: Modelling agent-environment interaction in multi-agent simulations with affordances. Ph.D. thesis, Melbourne School of Engineering University of Melbourne (2009)Google Scholar
  32. 32.
    Paris, S., Donikian, S.: Activity-driven populace: a cognitive approach to crowd simulation. IEEE Comput. Graph. Appl. 29(4), 34–43 (2009)CrossRefGoogle Scholar
  33. 33.
    Raubal, M.: Ontology and epistemology for agent-based wayfinding simulation. Int. J. Geogr. Inf. Sci. 15, 653–665 (2001)CrossRefGoogle Scholar
  34. 34.
    Raubal, M., Moratz, R.: A functional model for affordance-based agents. In: Rome, E., Hertzberg, J., Dorffner, G. (eds.) Towards Affordance-Based Robot Control, Lecture Notes in Computer Science, vol. 4760, pp. 91–105. Springer, Berlin, Heidelberg (2008)Google Scholar
  35. 35.
    Ricci, A., Piunti, M., Viroli, M.: Environment programming in multi-agent systems: an artifact-based perspective. Journal Autonomous Agents and Multi-Agent Systems 23, 158–192 (2011)CrossRefGoogle Scholar
  36. 36.
    Şahin, E., Çakmak, M., Dogar, M.R., Ugur, E., Ücoluk, G.: To afford or not to afford: a new formalism of affordances towards affordance-based robot control. Adapt. Behav. 15(4), 447–472 (2007)CrossRefGoogle Scholar
  37. 37.
    Schneider, S.: Grounding geographic information in perceptual operations, vol. 244. IOS Press (2012)Google Scholar
  38. 38.
    Shaw, R.: The agent-environment interface: Simon’s indirect or Gibson’s direct coupling. Ecol. Psychol. 15(1), 37–106 (2003)CrossRefGoogle Scholar
  39. 39.
    Ugur, E., Şahin, E., Oztop, E.: Unsupervised Learning of Object Affordances for Planning in a Mobile Manipulation Platform. In: IEEE International Conference on Robotics and Automation (ICRA), 2011, pp. 4312–4317 (2011)Google Scholar
  40. 40.
    Weiss, G.: Multiagent Systems, 2nd edn. MIT Press (2013)Google Scholar
  41. 41.
    Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Auton. Agent. Multi-Agent Syst. 14(1), 5–30 (2007)CrossRefGoogle Scholar
  42. 42.
    Wooldridge, M.J.: Introduction to Multiagent Systems, 2nd edn. Wiley (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Science and TechnologyÖrebro UniversityÖrebroSweden

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