Skip to main content

Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model

  • Conference paper
  • First Online:
Optimization, Learning Algorithms and Applications (OL2A 2022)


Agent-based and individual-based modeling have been widely used to simulate ecological systems. The historical architectures designed to artificial life simulation, namely LIDA and MicroPsy, rely into classical concurrence mechanisms based on threads, shared memory and locks. Although these mechanisms seem to work fine for many multi-agent systems (MAS), notably for those requiring synchronous communication between agents, they present severe restrictions in case of complex asynchronous MAS. In this work, we explore an alternative approach to handle concurrency in distributed asynchronous MAS: the actor model. An actor is a concurrent entity capable of sending, receiving and handling asynchronous messages, and creating new actors. Within this paradigm, there are no shared memory and, hence, no data race conditions. We introduce L2L (a short for: Learn to Live, Live to Learn) architecture, a biological inspired distributed non-deterministic MAS simulation framework, in which the autonomous agents (creatures) are endowed with a functional and minimal nervous system model enabling them to learn from its own experiences and interactions with the two-dimensional world, populated with creatures and nutrients. Both creatures and nutrients are encapsulated in actors. The system as a whole performs as a discrete non-deterministic dynamical system, as well as the creatures themselves. The scalability of this actor-based framework is evaluated showing the system scales up and out − many processes per processor node and in a computer cluster. A second experiment is realized to validate the architecture, consisting of an open-ended foraging simulation with both one or many creatures and hundreds of nutrients. Results from this specific actor-based version are compared to those from a classical concurrency version of the same architecture, showing they are equivalent, despite the fact that the former version scales a lot better. Moreover, results show that exploration of the world is unbiased, leading us to conjecture that our system follows ergodic hypothesis. We argue that the actor-based model proves to be very promising to modeling of asynchronous complex MAS.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others


  1. 1.

  2. 2.


  1. Beecham, J.A., Farnsworth, K.D.: Animal foraging from an individual perspective: an object orientated model. Ecol. Model. 113, 141–156 (1998)

    Article  Google Scholar 

  2. An, L.: Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol. Model. 229, 25–36 (2012)

    Article  Google Scholar 

  3. Bousquet, F., Le Page, C.: Multi-agent simulations and ecosystem management: a review. Ecol. Model. 176(3), 313–332 (2004)

    Article  Google Scholar 

  4. Friedlander, D., Franklinb. S.: LIDA and a theory of mind. In: Proceedings of the first AGI Conference on Artificial General Intelligence, vol. 171, p. 137. IOS Press (2008)

    Google Scholar 

  5. Bach, J.: The MicroPsi agent architecture. In: Proceedings of ICCM-5, International Conference on Cognitive Modeling, Bamberg, Germany, pp. 15–20. Citeseer (2003)

    Google Scholar 

  6. Bach, J.: MicroPsi 2: the next generation of the MicroPsi framework. In: Bach, J., Goertzel, B., Iklé, M. (eds.) AGI 2012. LNCS (LNAI), vol. 7716, pp. 11–20. Springer, Heidelberg (2012).

    Chapter  Google Scholar 

  7. Maturana, H.R.: Everything is said by an observer. In: Thompson, W.I. (ed.) Gaia: A Way of Knowing. Political Implications of the New Biology, pp. 11–36 (1987)

    Google Scholar 

  8. Hewitt, C. , Zenil, H.: What is computation? Actor model versus Turing’s model. In: A Computable Universe: Understanding and Exploring Nature as Computation, pp. 159–185 (2013)

    Google Scholar 

  9. Angiani, G., Fornacciari, P., Lombardo, G., Poggi, A., Tomaiuolo, M.: Actors based agent modelling and simulation. In: Bajo, J., et al. (eds.) PAAMS 2018. CCIS, vol. 887, pp. 443–455. Springer, Cham (2018).

    Chapter  Google Scholar 

  10. Parizi, M., Sileno, G., van Engers, T., Klous, S.: Run, agent, run! Architecture and benchmarking of actor-based agents. In: Proceedings of the 10th ACM SIGPLAN International Workshop on Programming Based on Actors, Agents, and Decentralized Control, pp. 11–20 (2020)

    Google Scholar 

  11. Leon, F.: ActressMAS, a. NET multi-agent framework inspired by the actor model. Mathematics 10(3), 382 (2022)

    Google Scholar 

  12. Crafa, S.: From agent-based modeling to actor-based reactive systems in the analysis of financial networks. J. Econ. Interact. Coord. 16(3), 649–673 (2021).

    Article  Google Scholar 

  13. Nguyen, H., Do, T., Rotter, C.: Optimizing the resource usage of actor-based systems. J. Netw. Comput. Appl. 190, 103143 (2021)

    Article  Google Scholar 

  14. Starzec, M., Starzec, G., Byrski, A., Turek, W.: Distributed ant colony optimization based on actor model. Parallel Comput. 90, 102573 (2019)

    Article  MathSciNet  Google Scholar 

  15. Idzik, M., Byrski, A., Turek, W., Kisiel-Dorohinicki, M.: Asynchronous actor-based approach to multiobjective hierarchical strategy. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12139, pp. 172–185. Springer, Cham (2020).

    Chapter  Google Scholar 

  16. Cao, D., Hu, W., Zhao, J., Huang, Q., Chen, Z., Blaabjerg, F.: A multi-agent deep reinforcement learning based voltage regulation using coordinated PV inverters. IEEE Trans. Power Syst. 35(5), 4120–4123 (2020)

    Article  Google Scholar 

  17. Li, J., Yu, T., Zhang, X.: Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning. Appl. Energy 306, 117900 (2022)

    Article  Google Scholar 

  18. Campos, L., Dickman, R., Graeff, F., Borges, H.: A concurrent, minimalist model for an embodied nervous system. In: International Brazilian Meeting on Cognitive Science (2015)

    Google Scholar 

  19. Hewitt, C.: Actor model of computation: scalable robust information systems. arXiv preprint arXiv:1008.1459 (2010)

  20. Armstrong, J.: Programming Erlang: software for a concurrent world. Pragmatic Bookshelf (2007)

    Google Scholar 

  21. Cornfeld, I.P., Fomin, S.V., Sinai, Y.G.: Ergodic Theory, vol. 245. Springer, New York (2012)

    MATH  Google Scholar 

  22. Yerkes, R.M., Dodson, J.D.: The relation of strength of stimulus to rapidity of habit-formation. J. Comp. Neurol. Psychol. 18(5), 459–482 (1908)

    Article  Google Scholar 

Download references


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754382. This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366) and by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). The authors thank UAH, UFRJ and CEFET-MG for the infrastructure, and Brazilian research agencies for partially support: CAPES (Finance Code 001), FAPERJ, FAPEMIG and National Council for Scientific and Technological Development - CNPq. “The content of this publication does not reflect the official opinion of the European Union. Responsibility for the information and views expressed herein lies entirely with the author(s).”

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Felipe D. Reis , Tales B. Nascimento , Carolina G. Marcelino , Elizabeth F. Wanner , Henrique E. Borges or Sancho Salcedo-Sanz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Reis, F.D., Nascimento, T.B., Marcelino, C.G., Wanner, E.F., Borges, H.E., Salcedo-Sanz, S. (2022). Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics