Two approaches to developing a multi-agent system for battle command simulation

Løvlid, Rikke Amilde; Alstad, Anders; Mevassvik, Ole Martin; Reus, Nico de; Henderson, H.C.; Vecht, Bob van der; Luik, Torec

doi:10.1109/wsc.2013.6721533

Cited by 5 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…33 Our two efforts were compared in a joint paper. 34 The conclusion was that both CxBR and BDI were suitable for developing command agents, but had different pros and cons. The benefit of using BDI-agents is that BDI is a well-known paradigm for agent modeling, with several available frameworks, which makes it easy to start with.…”

Section: Discussionmentioning

confidence: 99%

Modeling the behavior of a hierarchy of command agents with context-based reasoning

Løvlid

Bruvoll

Brathen

et al. 2017

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

Context-based reasoning is a paradigm for modeling agent behavior that is based on the idea that humans only use a small portion of their knowledge at any given time. It was specially designed to represent human tactical behavior and has been successfully implemented in systems with single agents or two agents working together. In this paper, we apply this idea in a hierarchical multi-agent system of command agents, where the agents' actions are to command and coordinate subordinates, send reports to their superiors, and communicate with other agents at the same level. We focus on how contexts and actions can be defined for these higher level command agents and how the contexts and actions for the different command agents are related. The proposed methodology is implemented and tested for a hierarchy of command agents that are interpreting and planning an operational order at a battalion level and carrying it out in a computer generated forces environment.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling the behavior of a hierarchy of command agents with context-based reasoning

Løvlid

Bruvoll

Brathen

et al. 2017

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

show abstract

“…When creating a scenario, domain knowledge must be communicated well for the agent to function properly. However, as the size of the unit grows, scenario creation becomes more complex, which makes it difficult to implement the scenario into a model [8].…”

Section: B Cgf Automation Methodology and Reinforcement Learningmentioning

confidence: 99%

“…Intention stands for the process of the agent selecting an action by considering the Belief and the Desire. Løvlid, et al [8] compared of the performances of the C2(Command and Control) system in cases when CxBR and BDI were respectively applied to it. There are other CGF studies done with CxBR and BDI, such as [16], [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental and Computational Study on the Ground Forces CGF Automation of Wargame Models Using Reinforcement Learning

et al. 2022

View full text Add to dashboard Cite

Wargame is an important tool that enables training units to develop various strategies by allowing them to experience unexpected situations. There are three methodologies that determine the behavior of the Computer Generated Forces(CGF) in wargame-rule-based, agent-based, and learningbased methodologies. The military determines the behaviors of the CGF mainly based on the rules because a doctrine and an operation plan are well established. However, the advent of intelligent weapons and the accompanying changes in tactics will make it difficult to expect an environment and situations of the future battlefield. Therefore, we studied the automation of CGF through reinforcement learning in order to give unexpected situations, so that the training unit would be able to establish various strategies and tactics through the wargame model. Based on the combat functions of the ground forces, we configured multiple environments that the ground forces CGFs will learn in. First, infantry and artillery CGFs learned in the close combat environment, which is the basis of ground forces combat. Second, the trainee CGF learned in the context of military training. Third, the drone CGF learned how to reconnaissance and attack in a multidrone environment, and finally, the combat service support CGF learned under the mission of supplying ammunition. As a result, we confirmed that the reinforcement learning methodology is applicable to CGF through these experiments.

show abstract

“…There are also some other approaches that target employing the C-BML in multi-agent simulations. As reported, 31 the Norwegian Defence Research Establishment uses Jadex, which is an agent development environment based on belief-desire-intention (BDI), 23 and Netherlands Organization for Applied Scientific Research uses context-based reasoning for developing multi-agent simulations for parsing orders issued by the commanders to low-level forces. 32 Each of them developed a multi-agent system that can process and decompose the orders issued by a commander to subordinate units according to military doctrine and concept by employing C-BML.…”

Section: Coalition Battle Management Languagementioning

confidence: 99%

Rapid prototyping of cognitive agent simulations using C-BML transformations

Topçu

Yılmaz

2019

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

Simulating battle management is an essential technique used in planning and mission rehearsal as well as training. Simulation development costs tend to be high due to the complexity of cognitive system architectures in such applications. Due to this complexity, it takes significant effort for a simulation developer to comprehend the problem domain enough to capture accurately in a simulation code. Domain-specific languages (DSL) play an important role in narrowing the communication gap between the domain user and the developer and hence facilitate rapid development. In command and control (C2) applications, the coalition battle management language (C-BML) serves as a DSL for exchanging battle information among C2 systems, simulations, and autonomous elements. In this article, we use a rapid prototyping framework for cognitive agents and demonstrate deployment of agent systems by adopting the model driven engineering approach. To this end, we extend the use of C-BML and automatically transform it in a cognitive agent model, which is then used for adaptive decision making at runtime. As a result, during a simulation run, it is possible to initialize and modify an agent’s goal reasoning model. The cognitive agent models are based on the deliberative coherence theory, which provides a goal reasoning system in terms of coherence-driven agents.

show abstract

Two approaches to developing a multi-agent system for battle command simulation

Cited by 5 publications

References 8 publications

Modeling the behavior of a hierarchy of command agents with context-based reasoning

Modeling the behavior of a hierarchy of command agents with context-based reasoning

Experimental and Computational Study on the Ground Forces CGF Automation of Wargame Models Using Reinforcement Learning

Rapid prototyping of cognitive agent simulations using C-BML transformations

Contact Info

Product

Resources

About