Skip to main content

Integration of Agents into HLA

  • Chapter
  • First Online:
Guide to Distributed Simulation with HLA

Abstract

We can identify two approaches for realizing synergies between multi-agent systems and distributed simulation. The first one is to incorporate agents into a simulation environment, specifically into an HLA federation, using interoperability techniques. The second is to build an agent environment based on HLA so that the RTI is employed to serve as the medium for agent communication and management. For preliminaries, we briefly introduce agent-based simulation and describe a cognitive agent architecture as an example agent architecture. Then, we discuss these two integration approaches.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Andersson, J., & Löf, S. (1999). HLA as conceptual basis for a multi-agent environment. Orlando: s.n.

    Google Scholar 

  • Bratman, M. (1987). Intention, plans, and practical reason. s.l.: Harvard University Press.

    Google Scholar 

  • Braubach, L., & Pokahr, A. (2009). Using rule-based concepts as foundation for higher-level agent architectures. In: Handbook of research on emerging rule-based languages and technologies: Open solutions and approaches (pp. 493–524). s.l.: s.n.

    Google Scholar 

  • CogAgentLib. (2017). CogAgentLib. https://sites.google.com/site/okantopcu/coagent. Accessed April 22, 2017.

  • DeCoAgent. (2015). DeCoAgent framework web site. https://sites.google.com/site/okantopcu/decoagent. Accessed 22 Apr 2017.

  • Finin, T., Fritzson, R., McKay, D., & McEntire, R. (1994). KQML as an agent communication language (pp. 456–463). Gaithersburg: ACM.

    Google Scholar 

  • FIPA-SC00008I. (2002). FIPA SL content language specification. Geneva, Switzerland: Foundation for intelligent physical agents.

    Google Scholar 

  • FIPA-SC00061G (2012). FIPA ACL message structure specification, Geneva, Switzerland: FIPA.

    Google Scholar 

  • Göktürk, E., & Polat, F. (2003). Implementing agent communication for a multi-agent simulation infrastructure on HLA (pp. 619–626). Paris: Springer.

    Google Scholar 

  • Haykin, S. (2009). Neural networks and learning machines. New Jersey: Pearson.

    Google Scholar 

  • Macal, C., & North, M. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4, 151–162.

    Article  Google Scholar 

  • McClelland, J. L. (2014). Explorations in parallel distributed processing: A handbook of models, programs, and exercises (2nd ed.) Stanford.

    Google Scholar 

  • Millgram, E., & Thagard, P. (1996). Deliberative coherence. Synthese, 108(1), 63–88.

    Article  MathSciNet  MATH  Google Scholar 

  • OMG. (2012). OMG systems modeling language (SysML) Version 1.3. s.l.: OMG.

    Google Scholar 

  • Poslad, S. (2007). Specifying protocols for multi-agent systems interaction. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2(4), 15:1–15:24.

    Google Scholar 

  • Russell, S., & Norvig, P. (2010). Artificial intelligence a modern approach (3rd ed.). Upper Saddle River (New Jersey): Prentice Hall.

    MATH  Google Scholar 

  • Shiffman, D. (2012). The nature of code. s.l.: s.n.

    Google Scholar 

  • Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences, 12(3), 435–502.

    Article  Google Scholar 

  • Thagard, P., & Millgram, E. (1995). Inference to the best plan: A coherence theory of decision. In A. Ram & D. B. Leake (Eds.), Goal-driven learning (pp. 439–454). Cambridge: MIT Press.

    Google Scholar 

  • Thagard, P., & Verbeurgt, K. (1998). Coherence as constraint satisfaction. Cognitive Science, 22(1), 1–24.

    Article  Google Scholar 

  • Topçu, O. (2014). Adaptive decision making in agent-based simulation. Journal of Simulation: Transactions of the Society for Modeling and Simulation International, 90(7), 815–832.

    Article  Google Scholar 

  • Zhuang, Y., Zhang, Z., Cheng, J., & Du, H. (2006). Research on multi-agent simulation environment based on HLA (pp. 154–159). Dalian: IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Okan Topçu .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Topçu, O., Oğuztüzün, H. (2017). Integration of Agents into HLA. In: Guide to Distributed Simulation with HLA. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-61267-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61267-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61266-9

  • Online ISBN: 978-3-319-61267-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics