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

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

doi:10.1007/978-3-031-23236-7_48

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

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

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

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.

Original language	English
Title of host publication	Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings
Editors	Ana I. Pereira, Andrej Košir, Florbela P. Fernandes, Maria F. Pacheco, João P. Teixeira, Rui P. Lopes
Publisher	Springer
Pages	701-713
ISBN (Electronic)	9783031232367
ISBN (Print)	9783031232350
DOIs	https://doi.org/10.1007/978-3-031-23236-7_48
Publication status	Published - 1 Jan 2023
Event	2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022 - Braganca, Portugal Duration: 24 Oct 2022 → 25 Oct 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1754 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022
Country/Territory	Portugal
City	Braganca
Period	24/10/22 → 25/10/22

Funding

Acknowledgment. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-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).” This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk̷lodowska-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).”

Keywords

Complex agents
Distributed MAS
Situated cognition

Access to Document

10.1007/978-3-031-23236-7_48

Cite this

Reis, F. D., Nascimento, T. B., Marcelino, C. G., Wanner, E. F., Borges, H. E., & Salcedo-Sanz, S. (2023). Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model. In A. I. Pereira, A. Košir, F. P. Fernandes, M. F. Pacheco, J. P. Teixeira, & R. P. Lopes (Eds.), Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings (pp. 701-713). (Communications in Computer and Information Science; Vol. 1754 CCIS). Springer. https://doi.org/10.1007/978-3-031-23236-7_48

Reis, Felipe D. ; Nascimento, Tales B. ; Marcelino, Carolina G. et al. / Asynchronous and Distributed Multi-agent Systems : An Approach Using Actor Model. Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings. editor / Ana I. Pereira ; Andrej Košir ; Florbela P. Fernandes ; Maria F. Pacheco ; João P. Teixeira ; Rui P. Lopes. Springer, 2023. pp. 701-713 (Communications in Computer and Information Science).

@inproceedings{8881f350e5114c9ebf8ab804d285f14b,

title = "Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model",

abstract = "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.",

keywords = "Complex agents, Distributed MAS, Situated cognition",

author = "Reis, \{Felipe D.\} and Nascimento, \{Tales B.\} and Marcelino, \{Carolina G.\} and Wanner, \{Elizabeth F.\} and Borges, \{Henrique E.\} and Sancho Salcedo-Sanz",

year = "2023",

month = jan,

day = "1",

doi = "10.1007/978-3-031-23236-7\_48",

language = "English",

isbn = "9783031232350",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "701--713",

editor = "Pereira, \{Ana I.\} and Andrej Ko{\v s}ir and Fernandes, \{Florbela P.\} and Pacheco, \{Maria F.\} and Teixeira, \{Jo{\~a}o P.\} and Lopes, \{Rui P.\}",

booktitle = "Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings",

address = "Germany",

note = "2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022 ; Conference date: 24-10-2022 Through 25-10-2022",

}

Reis, FD, Nascimento, TB, Marcelino, CG, Wanner, EF, Borges, HE & Salcedo-Sanz, S 2023, Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model. in AI Pereira, A Košir, FP Fernandes, MF Pacheco, JP Teixeira & RP Lopes (eds), Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings. Communications in Computer and Information Science, vol. 1754 CCIS, Springer, pp. 701-713, 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022, Braganca, Portugal, 24/10/22. https://doi.org/10.1007/978-3-031-23236-7_48

Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model. / Reis, Felipe D.; Nascimento, Tales B.; Marcelino, Carolina G. et al.
Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings. ed. / Ana I. Pereira; Andrej Košir; Florbela P. Fernandes; Maria F. Pacheco; João P. Teixeira; Rui P. Lopes. Springer, 2023. p. 701-713 (Communications in Computer and Information Science; Vol. 1754 CCIS).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Asynchronous and Distributed Multi-agent Systems

T2 - 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022

AU - Reis, Felipe D.

AU - Nascimento, Tales B.

AU - Marcelino, Carolina G.

AU - Wanner, Elizabeth F.

AU - Borges, Henrique E.

AU - Salcedo-Sanz, Sancho

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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.

KW - Complex agents

KW - Distributed MAS

KW - Situated cognition

UR - https://www.scopus.com/pages/publications/85148042024

UR - https://link.springer.com/chapter/10.1007/978-3-031-23236-7_48

U2 - 10.1007/978-3-031-23236-7_48

DO - 10.1007/978-3-031-23236-7_48

M3 - Conference publication

AN - SCOPUS:85148042024

SN - 9783031232350

T3 - Communications in Computer and Information Science

SP - 701

EP - 713

BT - Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings

A2 - Pereira, Ana I.

A2 - Košir, Andrej

A2 - Fernandes, Florbela P.

A2 - Pacheco, Maria F.

A2 - Teixeira, João P.

A2 - Lopes, Rui P.

PB - Springer

Y2 - 24 October 2022 through 25 October 2022

ER -

Reis FD, Nascimento TB, Marcelino CG, Wanner EF, Borges HE, Salcedo-Sanz S. Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model. In Pereira AI, Košir A, Fernandes FP, Pacheco MF, Teixeira JP, Lopes RP, editors, Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Proceedings. Springer. 2023. p. 701-713. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-23236-7_48