Next-generation readiness: applying AI to military simulators and wargaming in Finland

This article examines the potential of artificial intelligence (AI) to enhance the performance, adaptability, and decision-making value of simulation systems currently used by the Finnish Defence Forces, particularly within the land forces. It reviews how Large Language Models (LLMs), generative agents, and edge AI can be applied to tactical simulators, command post exercises, and educational platforms to support training, mission planning, and after-action review (AAR) processes. The study highlights current systems such as SAAB Live Training Simulator (in Finnnish KASI), SAAB GC IDT (Ground Combat Indoor Trainer, in Finnnish SAS), VBS (Virtual Battlespace), and MASA SWORD (in Finnnish KESI), and identifies key limitations related to realism, scenario documentation, and scalability. It then presents a framework for AI integration through natural language interfaces, skill-based agent models, and offline generative AI deployed on edge devices. Use cases include COA generation, automated AAR synthesis, adversary red-teaming, and scenario scripting from natural language inputs. The article concludes by recommending pilot initiatives for AI-enhanced simulation development in Finland and proposes avenues for NATO-aligned research cooperation. These findings suggest that AI offers a path to next-generation readiness by supporting decentralized operations, reducing instructor workload, and fostering doctrinal adaptability through intelligent, embedded simulation tools.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.09.473
Primary Topic
Multi-Agent Systems and Negotiation
Type
article
Field-Weighted Citation Impact
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article

Next-generation readiness: applying AI to military simulators and wargaming in Finland

Kalle Saastamoinen, Jussi Nuutinen
Procedia Computer Science
Multi-Agent Systems and Negotiation
article

Next-generation readiness: applying AI to military simulators and wargaming in Finland

Kalle Saastamoinen, Jussi Nuutinen
article en

Abstract

This article examines the potential of artificial intelligence (AI) to enhance the performance, adaptability, and decision-making value of simulation systems currently used by the Finnish Defence Forces, particularly within the land forces. It reviews how Large Language Models (LLMs), generative agents, and edge AI can be applied to tactical simulators, command post exercises, and educational platforms to support training, mission planning, and after-action review (AAR) processes. The study highlights current systems such as SAAB Live Training Simulator (in Finnnish KASI), SAAB GC IDT (Ground Combat Indoor Trainer, in Finnnish SAS), VBS (Virtual Battlespace), and MASA SWORD (in Finnnish KESI), and identifies key limitations related to realism, scenario documentation, and scalability. It then presents a framework for AI integration through natural language interfaces, skill-based agent models, and offline generative AI deployed on edge devices. Use cases include COA generation, automated AAR synthesis, adversary red-teaming, and scenario scripting from natural language inputs. The article concludes by recommending pilot initiatives for AI-enhanced simulation development in Finland and proposes avenues for NATO-aligned research cooperation. These findings suggest that AI offers a path to next-generation readiness by supporting decentralized operations, reducing instructor workload, and fostering doctrinal adaptability through intelligent, embedded simulation tools.

Procedia Computer ScienceVol. 270
Lappeenranta-Lahti University of Technology (FI)
Openalex Percentile: Top 83%
Multi-Agent Systems and Negotiation
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