Skip to main content
Log in

Integrating BDI Agents with Agent-Based Simulation Platforms

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

Agent-based models (ABMs) are increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire-Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the “brains” of an agent can be modelled in the BDI system in the usual way, while the “body” exists in the ABM system. The architecture is flexible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration of off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. The support infrastructure, along with the code required for a number of specific systems and several example applications, is freely available at http://tiny.cc/bdi-abm-integration.

  2. In some ABMs, the environment consists solely of other agents and the percepts and actions available to the agents are limited to the exchange of messages. However in this paper, we focus on spatially explicit ABMs.

  3. We call these actions BDI Actions to distinguish them from actions in the ABM which may include lower-level actions.

  4. Suspension is not yet fully implemented in the publicly available software.

  5. www.openstreetmap.org.

  6. http://services.land.vic.gov.au/landchannel/content/productCatalogue.

  7. http://www.abs.gov.au/websitedbs/censushome.nsf/home/communityprofiles.

  8. www.anylogic.com.

References

  1. Axelrod, R. M. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton Univeristy Press.

    Google Scholar 

  2. Balke, T., & Gilbert, N. (2014). How do agents make decisions? A survey. Journal of Artificial Societies and Social Simulation (JASSS), 17(4), 13.

    Article  Google Scholar 

  3. Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., Axhausen, K. (2009). MATSim-T: Architecture and simulation times. Multi-agent Systems for Traffic and Transportation Engineering, 57–78.

  4. Behrens, T. M., Dastani, M., Dix, J., Hübner, J., Köster, M., Novák, P., et al. (2012). The multi-agent programming contest. AI Magazine, 33(4), 111–113.

    Google Scholar 

  5. Benfield, S. S., Hendrickson, J., & Galanti, D. (2006). Making a strong business case for multiagent technology. Autonomous agents and multi-agent systems (AAMAS) (pp. 10–15). Hakodate: ACM Press.

    Google Scholar 

  6. Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747–761.

    Article  Google Scholar 

  7. Bordini, R.H., Hübner, J.F. (2005). BDI agent programming in AgentSpeak using Jason. In: International Workshop on Computational Logic in Multi-Agent Systems (CLIMA) (pp. 143–164). London, UK.

  8. Bordini, R. H., & Hübner, J. F. (2009). Agent-based simulation using BDI programming in Jason. Multi-agent systems: simulation and applications (pp. 451–471). Boca Raton: CRC Press.

    Google Scholar 

  9. Bordini, R.H., Hübner, J.F., Wooldridge, M. (2007) Programming multi-agent Systems in AgentSpeak using Jason. Wiley series in agent technology. Hoboken: Wiley. ISBN: 0470029005

  10. Bratman, M. E. (1987). Intentions, plans, and practical reason. Massachusetts: Harvard University Press.

    Google Scholar 

  11. Braubach, L., Pokahr, A., & Lamersdorf, W. (2005). Jadex: A BDI agent system combining middleware and reasoning. Software agent-based applications, platforms and development kits (pp. 143–168). Boston: Springer.

    Chapter  Google Scholar 

  12. Busetta, P., Rönnquist, R., Hodgson, A., Lucas, A. (1998). JACK intelligent agents - components for intelligent agents in Java. Technical report, AOS Pty. Ltd, Melbourne, Australia.

  13. Dastani, M. (2008). 2APL: A practical agent programming language. Autonomous Agents and Multi-Agent Systems, 16(3), 214–248.

    Article  Google Scholar 

  14. Dennett, D. C. (1987). The intentional stance. Cambridge: MIT Press.

    Google Scholar 

  15. Dignum, V., Vázquez-Salceda, J., Dignum, F. (2004). OMNI: Introducing social structure, norms and ontologies into agent organizations. In Programming multiagent systems languages, frameworks, techniques and tools workshop (PROMAS). (pp. 181–198). Selected revised and invited papers.

  16. Epstein, J. (2006). Generative social science—studies in agent-based computational modeling. Princeton: Princeton University Press.

    MATH  Google Scholar 

  17. Ferber, J. (1999). Multi-Agent Systems. Boston: Addison Wesley Longman.

    MATH  Google Scholar 

  18. GAMS Development Corporation: General algebraic modeling system (gams) website. Retrieved from March 20, 2015 from http://www.gams.com/.

  19. Geard, N., McCaw, J. M., Dorin, A., Korb, K. B., & McVernon, J. (2013). Synthetic population dynamics: A model of household demography. Journal of Artificial Societies and Social Simulation, 16(1), 8.

    Article  Google Scholar 

  20. Geard, N., Singh, D., McVernon, J., Padgham, L. (2013). A model of parental decision making and behaviour about childhood vaccination. In Epidemics 4: Fourth international conference on infectious disease dynamics (pp. 19–22). Amsterdam.

  21. Georgeff, M., Ingrand, F. (1989). Decision making in an embedded reasoning system. In: International joint conference on artificial intelligence (IJCAI) (pp. 972–978). Detroit.

  22. Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. New York: McGraw-Hill International.

    Google Scholar 

  23. Hindriks, K. V. (2009). Programming rational agents in GOAL. Multi-agent programming: languages, tools and applications (pp. 119–157). New York: Springer.

    Chapter  Google Scholar 

  24. IEEE (2000). IEEE Standard for modeling and simulation (M&S) High Level Architecture (HLA)—Framework and rules. IEEE Standard No.: 1516-2000. IEEE, Piscataway

  25. Iftekhar, M., Hailu, A., & Lindner, R. (2014). Does it pay to increase competition in combinatorial conservation auctions? Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 62(3), 411–433.

    Article  Google Scholar 

  26. Jarvis, D., Jarvis, J., Rnnquist, R., & Jain, L. C. (2013). Multiagent systems and applications, development using the GORITE BDI framework. Intelligent Systems Reference Library (Vol. 46). New York: Springer.

    Google Scholar 

  27. Kaminka, G. A., Veloso, M. M., Schaffer, S., Sollitto, C., Adobbati, R., Marshall, A. N., et al. (2002). GameBots: A flexible test bed for multiagent team research. Communications of the ACM, 45(1), 43–45.

    Article  Google Scholar 

  28. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E. (1997). RoboCup: The robot world cup initiative. In First international conference on autonomous agents (Agents’97) (pp. 340–347). Marina del Rey.

  29. Kitano, H., & Tadokoro, S. (2001). Robocup rescue: A grand challenge for multiagent and intelligent systems. AI Magazine, 22(1), 39–52.

    Google Scholar 

  30. Kuhl, F., Weatherly, R., & Dahmann, J. (1999). Creating computer simulation systems: An introduction to the high level architecture. Upper Saddle River: Prentice Hall.

    MATH  Google Scholar 

  31. Lees, M., Logan, B., & Theodoropoulos, G. (2007). Distributed simulation of agent-based systems with HLA. ACM Transactions on Modeling and Computer Simulation, 17(3), 11.

    Article  Google Scholar 

  32. Minson, R., & Theodoropoulos, G. K. (2008). Distributing repast agent-based simulations with hla. Concurrency and Computation: Practice and Experience, 20(10), 1225–1256.

    Article  Google Scholar 

  33. Mittal, S., & Douglass, S. A. (2011). Net-centric ACT-R-based cognitive architecture with DEVS unified process. In Symposium on theory of modeling & simulation: DEVS integrative M&S symposium, TMS-DEVS’11 (pp. 34–44). Boston.

  34. North, M. J., Howe, T. R., Collier, N. T., & Vos, J. R. (2007). Advancing social simulation: The first world congress. A declarative model assembly infrastructure for verification and validation (pp. 129–140). Tokyo: Springer.

    Google Scholar 

  35. van Oijen, J. (2014). Cognitive agents in virtual worlds: A middleware design approach. Ph.D. thesis, Utrecht University.

  36. Padgham, L., Horne, R., Singh, D., & Moore, T. (2014). Planning for sandbagging as a response to flooding: A tool and case study. Australian Journal of Emergency Management (AJEM), 29, 26–31.

    Google Scholar 

  37. Padgham, L., Nagel, K., Singh, D., Chen, Q. (2014). Integrating bdi agents into a matsim simulation. Frontiers in Artificial Intelligence and Applications 263(ECAI 2014), 681–686

  38. Padgham, L., Scerri, D., Jayatilleke, G.B., Hickmott, S.L. (2011). Integrating BDI reasoning into agent based modeling and simulation. In: Winter simulation conference (WSC) (pp. 345–356).

  39. Rao, A., Georgeff, M. (1991). Modeling rational agents within a BDI-architecture. In Principles of knowledge representation and reasoning (KR) (pp. 473–484). Cambridge, MA.

  40. Rao, A. S. (1996). AgentSpeak (L): BDI agents speak out in a logical computable language. Agents breaking away (pp. 42–55). Berlin: Springer.

    Chapter  Google Scholar 

  41. Rao, A.S., Georgeff, M.P. (1995). BDI agents: From theory to practice. In International conference on multi-agent systems (ICMAS) (pp. 312–319). San Francisco.

  42. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. In 14th Annual conference on computer graphics and interactive techniques, SIGGRAPH ’87 (pp. 25–34). New York.

  43. Russell, S. J., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). Upper Saddle River: Pearson Education.

    MATH  Google Scholar 

  44. Sakellariou, I., Kefalas, P., & Stamatopoulou, I. (2008). Enhancing NetLogo to simulate BDI communicating agents. Artificial intelligence: Theories, models and applications (Vol. 5138, pp. 263–275)., Lecture Notes in Computer Science Springer: Berlin.

    Chapter  Google Scholar 

  45. Sardiña, S., & Padgham, L. (2011). A BDI agent programming language with failure handling, declarative goals, and planning. Autonomous Agents and Multi-Agent Systems, 23(1), 18–70.

    Article  Google Scholar 

  46. Scerri, D., Hickmott, S., Bosomworth, K., & Padgham, L. (2012). Using modular simulation and agent based modelling to explore emergency management scenarios. Australian Journal of Emergency Management (AJEM), 27, 44–48.

    Google Scholar 

  47. Shendarkar, A., Vasudevan, K., Lee, S., Son, Y.J. (2006). Crowd simulation for emergency response using BDI agent based on virtual reality. In Winter simulation conference (WSC) (pp. 545–553). Monterey.

  48. Sierhuis, M., Clancey, W. J., & van Hoof, R. J. J. (2007). Brahms: A multiagent modelling and simulation environment for work processes and practices. International Journal of Simulation and Process Modelling, 3(3), 134–152.

    Article  Google Scholar 

  49. Singh, D., Padgham, L. (2015). Community evacuation planning for bushfires using agent-based simulation (demonstration). In Autonomous agents and multi-agent systems (AAMAS) (pp. 1903–1904). Istanbul.

  50. Singh, D., Sardina, S., Padgham, L., James, G. (2011). Integrating learning into a BDI agent for environments with changing dynamics. In International joint conference on artificial intelligence (IJCAI) (pp. 2525–2530). Barcelona.

  51. Sloman, A., & Poli, R. (1996). SIM AGENT: A toolkit for exploring agent designs. Intelligent agents II: Agent theories architectures and languages (ATAL-95) (pp. 392–407). New York: Springer.

    Chapter  Google Scholar 

  52. Tambe, M. (1997). Towards flexible teamwork. Journal of Artificial Intelligence Research, 7, 83–124.

    Google Scholar 

  53. Tambe, M., Johnson, W. L., Jones, R. M., Koss, F. V., Laird, J. E., Rosenbloom, P. S., et al. (1995). Intelligent agents for interactive simulation environments. AI Magazine, 16(1), 15–39.

    Google Scholar 

  54. Taylor, G., Frederiksen, R., III, R.R.V., Waltz, E. (2004). Agent-based simulation of geo-political conflict. In: Conference on innovative applications of artificial intelligence (IAAI) (pp. 884–891). San Jose.

  55. Tidhar, G., Heinze, C., & Selvestrel, M. C. (1998). Flying together: Modelling air mission teams. Applied Intelligence, 8(3), 195–218.

    Article  Google Scholar 

  56. Tisue, S., Wilensky, U. (2004). Netlogo: A simple environment for modeling complexity. In International conference on complex systems (ICCS) (pp. 16–21). Boston.

  57. Tolhurst, K., Shields, B., & Chong, D. (2008). Phoenix: development and application of a bushfire risk management tool. Australian Journal of Emergency Management (AJEM), 23(4), 47–54.

    Google Scholar 

  58. Winikoff, M. (2005). JACK intelligent agents: An industrial strength platform. Multi-agent programming: languages, platforms and applications, multiagent systems, artificial societies, and simulated organizations (Vol. 15, pp. 175–193). New York: Springer.

    Chapter  Google Scholar 

  59. Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000). Theory of modeling and simulation (2nd ed.). Cambridge: Academic Press.

    MATH  Google Scholar 

  60. Zhang, M., Verbraeck, A. (2014). A composable PRS-based agent meta-model for multi-agent simulation using the DEVS framework. In Symposium on Agent Directed Simulation, ADS’14 (pp. 1:1–1:8).

Download references

Acknowledgments

We would like to thank Kai Nagel, Sarah Bekessey, Fiona Fidler, Ascelin Gordon, Sayed Iftekhar, Nic Geard, Carole Adam, Todd Mason, Sewwandi Perera, Edmund Kemsley, Oscar Francis, Daniel Kidney, Thomas Wood, Andreas Suekto, Qingyu Chen, Arie Wilsher, Sarah Hickmott, and Dave Scerri for their contribution to the various platform integrations and applications discussed in this paper. We thank AOS for supporting this work through the provision of their JACK agent system for research purposes. Supported by ARC Discovery DP1093290, ARC Linkage LP130100008, and Telematics Trust Grants

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lin Padgham.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, D., Padgham, L. & Logan, B. Integrating BDI Agents with Agent-Based Simulation Platforms. Auton Agent Multi-Agent Syst 30, 1050–1071 (2016). https://doi.org/10.1007/s10458-016-9332-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-016-9332-x

Keywords

Navigation