A Language for Agent-based Discrete-event Modeling and Simulation of Linked Lives

In agent-based modeling and simulation, discrete-time methods prevail. While there is a need to cover the agents’ dynamics in continuous time, commonly used agent-based modeling frameworks offer little support for discrete-event simulation. Here, we present a formal syntax and semantics of the language ML3 (Modeling Language for Linked Lives) for modeling and simulating multi-agent systems as discrete-event systems. The language focuses on applications in demography, such as migration processes, and considers this discipline’s specific requirements. These include the importance of life courses being linked and the age-dependency of activities and events. The developed abstract syntax of the language combines the metaphor of agents with guarded commands. Its semantics is defined in terms of Generalized Semi-Markov Processes. The concrete language has been realized as an external domain-specific language. We discuss implications for efficient simulation algorithms and elucidate benefits of formally defining domain-specific languages for modeling and simulation.

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RCR Report of “A Language for Agent-Based Discrete-Event Modeling and Simulation of Linked Lives”

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3490030 ◽

2022 ◽

Vol 32 (1) ◽

pp. 1-4

Author(s):

Romolo Marotta

Keyword(s):

Modeling And Simulation ◽

Discrete Event ◽

Agent Based ◽

Event Modeling ◽

Term Retention ◽

Linked Lives ◽

Long Term Retention

The artifact evaluated in this report is relevant to the article. In fact, it allows us to run the experiments and reproduce figures, and the dependencies are documented. The process to regenerate data presented in the article completes correctly, and the results are reproducible. Additionally, the authors have uploaded their artifact on permanent repositories, which ensures a long-term retention. This article can thus receive the Artifacts Available , Artifacts Evaluated–Reusable , and Results Reproduced badges.

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Approaches to Modeling the Gas-Turbine Maintenance Process

Journal of Computing and Information Science in Engineering ◽

10.1115/1.3647876 ◽

2011 ◽

Vol 12 (1) ◽

Author(s):

Tai-Tuck Yu ◽

James P. Scanlan ◽

Richard M. Crowder ◽

Gary B. Wills

Keyword(s):

Decision Making ◽

Gas Turbine ◽

Discrete Event ◽

Agent Based Modeling ◽

Agent Based ◽

Event Modeling ◽

Human Decision ◽

Event Model ◽

Discrete Event Model

Discrete-event modeling has long been used for logistics and scheduling problems, while multi-agent modeling closely matches human decision-making process. In this paper, a metric-based comparison between the traditional discrete-event and the emerging agent-based modeling approaches is reported. The case study involved the implementation of two functionally identical models based on a realistic, nontrivial, civil aircraft gas turbine global repair operation. The size, structural complexity, and coupling metrics from the two models were used to gauge the benefits and drawbacks of each modeling paradigm. The agent-based model was significantly better than the discrete-event model in terms of execution times, scalability, understandability, modifiability, and structural flexibility. In contrast, and importantly in an engineering context, the discrete-event model guaranteed predictable and repeatable results and was comparatively easy to test because of its single-threaded operation. However, neither modeling approach on its own possesses all these characteristics nor can each handle the wide range of resolutions and scales frequently encountered in problems exemplified by the case study scenario. It is recognized that agent-based modeling can emulate high-level human decision-making and communication closely while discrete-event modeling provides a good fit for low-level sequential processes such as those found in manufacturing and logistics.

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