The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.
M. Baldoni, C. Baroglio, R. Micalizio, and S. Tedeschi, “Accountability in multi-agent organizations: from conceptual design to agent programming,” Auton. Agent. Multi. Agent. Syst., vol. 37, no. 1, 2023.
Haldorai, A. Ramu, and S. A. R. Khan, Eds., “Business Intelligence for Enterprise Internet of Things,” EAI/Springer Innovations in Communication and Computing, 2020, doi: 10.1007/978-3-030-44407-5.
E. Begoli, “Procedural Reasoning System (PRS) architecture for agent-mediated behavioral interventions,” in IEEE SOUTHEASTCON 2014, 2014.
M. d’Inverno and M. Luck, “Computational Architecture for BDI Agents,” in Springer Series on Agent Technology, Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 155–165.
A. Najjar et al., “Model transformations from the SARL agent-oriented programming language to an object-oriented programming language,” Int. J. Agent-oriented Softw. Eng., vol. 7, no. 1, p. 37, 2019.
N. B. Othman et al., “SUMMIT: A multi-modal agent-based co-simulation of urban public transport with applications in contingency planning,” Simul. Model. Pract. Theory, vol. 126, no. 102760, p. 102760, 2023.
M. Raees, T. A. Khan, K. Mustafa Abbasi, A. Ahmed, S. Fazilat, and I. Ahmed, “Context-aware services using MANETs for long-distance vehicular systems: A cognitive agent-based model,” Sci. Program., vol. 2021, pp. 1–12, 2021.
R. W. Collier, S. Russell, and D. Lillis, “Reflecting on agent programming with AgentSpeak(L),” in PRIMA 2015: Principles and Practice of Multi-Agent Systems, Cham: Springer International Publishing, 2015, pp. 351–366.
Haldorai and U. Kandaswamy, “Intelligent Spectrum Handovers in Cognitive Radio Networks,” EAI/Springer Innovations in Communication and Computing, 2019, doi: 10.1007/978-3-030-15416-5.
M. van Steen and A. S. Tanenbaum, “A brief introduction to distributed systems,” Computing, vol. 98, no. 10, pp. 967–1009, 2016.
G. Petrosino, E. Iotti, S. Monica, and F. Bergenti, “A description of the jadescript type system,” in Lecture Notes in Computer Science, Cham: Springer International Publishing, 2022, pp. 206–220.
R. C. Cardoso and A. Ferrando, “A review of agent-based programming for multi-agent systems,” Computers, vol. 10, no. 2, p. 16, 2021.M. A. Hashmi, M. U. Akram, and A. El Fallah-Seghrouchni, “PLACE: Planning based language for agents and computational environments,” in Engineering Multi-Agent Systems, Cham: Springer International Publishing, 2018, pp. 142–158.
M. A. Hashmi, M. U. Akram, and A. El Fallah-Seghrouchni, “PLACE: Planning based language for agents and computational environments,” in Engineering Multi-Agent Systems, Cham: Springer International Publishing, 2018, pp. 142–158.
“PLASA: Programming language for synchronous agents,” 2018.
M. Alcántara, A. Castañeda, D. Flores-Peñaloza, and S. Rajsbaum, “The topology of look-compute-move robot wait-free algorithms with hard termination,” Distrib. Comput., vol. 32, no. 3, pp. 235–255, 2019.
A. Krzywicki and W. Wobcke, “Empirical software engineering for evaluating adaptive task-oriented personal assistants: a case study in human/machine event extraction and coding,” Int. J. Agent-oriented Softw. Eng., vol. 7, no. 2, p. 184, 2022.
W. Wobcke and A. Krzywicki, “Empirical software engineering for evaluating adaptive task-oriented personal assistants: a case study in human/machine event extraction and coding,” Int. J. Agent-oriented Softw. Eng., vol. 7, no. 2, p. 184, 2022.
Haldorai and A. Ramu, “The Impact of Big Data Analytics and Challenges to Cyber Security,” Advances in Information Security, Privacy, and Ethics, pp. 300–314, 2018, doi: 10.4018/978-1-5225-4100-4.ch016.
D. Singh, L. Padgham, and B. Logan, “Integrating BDI agents with agent-based simulation platforms,” Auton. Agent. Multi. Agent. Syst., vol. 30, no. 6, pp. 1050–1071, 2016.
Y. Sánchez, T. Coma, A. Aguelo, and E. Cerezo, “ABC-EBDI: An affective framework for BDI agents,” Cogn. Syst. Res., vol. 58, pp. 195–216, 2019.
P. Caillou, B. Gaudou, A. Grignard, C. Q. Truong, and P. Taillandier, “A simple-to-use BDI architecture for agent-based modeling and simulation,” in Advances in Intelligent Systems and Computing, Cham: Springer International Publishing, 2017, pp. 15–28.
A. Ferrein, C. Maier, C. Mühlbacher, T. Niemueller, G. Steinbauer, and S. Vassos, “Controlling logistics robots with the action-based language YAGI,” in Intelligent Robotics and Applications, Cham: Springer International Publishing, 2016, pp. 525–537.
F. Kaptein, J. Broekens, K. V. Hindriks, and M. Neerincx, “CAAF: A Cognitive Affective Agent Programming Framework,” in Intelligent Virtual Agents, Cham: Springer International Publishing, 2016, pp. 317–330.
G. Santos et al., “Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling,” Energy Convers. Manag., vol. 99, pp. 387–399, 2015.
I. García-Magariño, G. Palacios-Navarro, and R. Lacuesta, “TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions,” Simul. Model. Pract. Theory, vol. 77, pp. 84–107, 2017.
S. Galland, S. Rodriguez, and N. Gaud, “Run-time environment for the SARL agent-programming language: the example of the Janus platform,” Future Gener. Comput. Syst., vol. 107, pp. 1105–1115, 2020.
A. Anjum, F. Sun, L. Wang, and J. Orchard, “A novel neural network-based symbolic regression method: Neuro-encoded expression programming,” arXiv [cs.NE], 2019.
S. Leask and B. Logan, “Programming agent deliberation using procedural reflection,” Fundam. Inform., vol. 158, no. 1–3, pp. 93–120, 2018.
L. da Silva Medeiros, R. E. Julio, R. M. A. de Almeida, and G. S. Bastos, “Enabling real-time processing for ROS2 embedded systems,” in Studies in Computational Intelligence, Cham: Springer International Publishing, 2019, pp. 477–528.
A. R. Panisson and R. H. Bordini, “Towards a computational model of argumentation schemes in agent-oriented programming languages,” in 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2020.
F. Ho and S. Nakadai, “Preference-based multi-objective multi-agent path finding,” Auton. Agent. Multi. Agent. Syst., vol. 37, no. 1, 2023.
E. S. van Haeringen, C. Gerritsen, and K. V. Hindriks, “Emotion contagion in agent-based simulations of crowds: a systematic review,” Auton. Agent. Multi. Agent. Syst., vol. 37, no. 1, 2023.
Z. Sun et al., “Simple or complicated agent-based models? A complicated issue,” Environ. Model. Softw., vol. 86, pp. 56–67, 2016.
C. C. Kerr et al., “Covasim: an agent-based model of COVID-19 dynamics and interventions,” bioRxiv, 2020.
M. W. Khan and J. Wang, “The research on multi-agent system for microgrid control and optimization,” Renew. Sustain. Energy Rev., vol. 80, pp. 1399–1411, 2017.
C. Christensen and J. Salmon, “An agent-based modeling approach for simulating the impact of small unmanned aircraft systems on future battlefields,” J. Def. Model. Simul. Appl. Methodol. Technol., vol. 19, no. 3, pp. 481–500, 2022.
M. A. Zolfagharipoor and A. Ahmadi, “Agent-based modeling of participants’ behaviors in an inter-sectoral groundwater market,” J. Environ. Manage., vol. 299, no. 113560, p. 113560, 2021.
J. Afara, V. Ajila, H. Macdonell, and P. Dobias, “Use of agent-based modeling to model intermediate force capabilities in (counter)mobility crowd scenarios,” J. Def. Model. Simul. Appl. Methodol. Technol., p. 154851292211417, 2022.
C. Adam and B. Gaudou, “BDI agents in social simulations: a survey,” Knowl. Eng. Rev., vol. 31, no. 3, pp. 207–238, 2016.
S. Berger, B. Häckel, and L. Häfner, “Organizing self-organizing systems: A terminology, taxonomy, and reference model for entities in cyber-physical production systems,” Inf. Syst. Front., vol. 23, no. 2, pp. 391–414, 2021.
V. Joevivek et al., “Spatial and temporal correlation between beach and wave processes: implications for bar–berm sediment transition,” Frontiers of Earth Science, vol. 12, no. 2, pp. 349–360, Jun. 2017, doi: 10.1007/s11707-017-0655-y.
A. Amirat, A. Hock-Koon, and M. C. Oussalah, “Object-oriented, component-based, agent-oriented and service-oriented paradigms in software architectures,” in Software Architecture 1, Chichester, UK: John Wiley & Sons, Ltd, 2014, pp. 1–53.
I. Schoon and J. Heckhausen, “Conceptualizing individual agency in the transition from school to work: A social-ecological developmental perspective,” Adolesc. Res. Rev., vol. 4, no. 2, pp. 135–148, 2019.
M. J. Ferguson and J. A. Bargh, “How social perception can automatically influence behavior,” Trends Cogn. Sci., vol. 8, no. 1, pp. 33–39, 2004.
C. Savaglio, M. Ganzha, M. Paprzycki, C. Bădică, M. Ivanović, and G. Fortino, “Agent-based Internet of Things: State-of-the-art and research challenges,” Future Gener. Comput. Syst., vol. 102, pp. 1038–1053, 2020.
R. H. Bordini, A. El Fallah Seghrouchni, K. Hindriks, B. Logan, and A. Ricci, “Agent programming in the cognitive era,” Auton. Agent. Multi. Agent. Syst., vol. 34, no. 2, 2020.
J. B. Larsen, “Going beyond BDI for agent-based simulation,” J. Inf. Telecommun., vol. 3, no. 4, pp. 446–464, 2019.
Anandakumar, H., and R. Arulmurugan. "Supervised, unsupervised and reinforcement learning-A detailed perspective." J Dyn Control Syst 11 (2019): 429-433.
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Ali-Кhusein
Ali-Кhusein
School of Computing, First Moscow State University, Russia.
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Cite this article
Ali-Кhusein, “An Analysis of Multi Agent Systems Agent Based Programming”, Journal of Computing and Natural Science, vol.3, no.4, pp. 182-193, October 2023. doi: 10.53759//181X/JCNS/202303017.
