Evaluation Management System

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BattleVerse – 1st Open call for experiments

Call identifier: BattleVerse – 1st Open call for experiments

Publication date: 2026-09-14

Status: Open

Opening date: 2026-09-14 12:00:00 (Brussels time)

Closing date: 2026-11-16 17:00:00 (Brussels time)

Call general details Thematic areas Supporting documentation

Call Summary

BATTLEVERSE conceives an innovative approach to successful mission planning and execution. By leveraging AI-driven scenario generation and reinforcement learning, BattleVerse bridges the gap between real and virtual battle environments, enabling the effective simulation of complex multi-domain mission scenarios. In this context, the BattleVerse Open Calls are designed to identify innovative technologies eligible for inclusion in the project’s ecosystem, targeting simulation software solutions that enhance mission planning and simulation frameworks. The BattleVerse Open Calls provide an opportunity for organisation with high-value solutions to participate in a dynamic environment, providing strategic technological and industrial innovations for a crucial field within the European Union.

Call Keywords

Modelling and Simulation as a Service
Mission Planning, Training and Execution
Multi-domain Operations and Simulations
Simulation and Digital Twins
Artificial Intelligence Systems
Machine Learning and Large Language Models
Human-in-the-loop Systems
Strategic Decision-making
Game Theory Modelling
Unmanned Vehicles
Autonomous Systems
Swarm Operations

BattleVerse

Technical Topics:

Topic 1: LLMs for strategic decision-making, symbolic reasoning, game theory, and mission planning under uncertainty

The objective of Topic 1 is to demonstrate the potential of Large Language Models (LLMs) in supporting military strategic decision-making and mission planning. In defence contexts, LLMs can contribute to the development and analysis of tactical scenarios, supporting the adaptation of operational strategies to evolving mission environments. The BattleVerse project aims to demonstrate how AI-based scenario generation tools can assist military personnel in planning and training activities while ensuring that human operators retain full authority and responsibility over operational decision-making.

Keywords:Artificial Intelligence SystemsMachine Learning and Large Language ModelsStrategic Decision-makingGame Theory Modelling

Topic 2: Multi-physics simulation capabilities for surface/submarine warfare, electronic warfare, and continuous combat across land, air, or naval platforms

The objective of Topic 2 is to explore and demonstrate advanced multi-physics simulation capabilities supporting military planning, training, and operational analysis across multiple domains, including surface and submarine warfare, electronic warfare, and joint operations across land, air, and naval platforms. Experiments funded under this topic should contribute to the development of simulation approaches capable of modelling complex operational scenarios and interactions between platforms, systems, and operational environments.

Keywords:Modelling and Simulation as a ServiceMission Planning, Training and ExecutionMulti-domain Operations and SimulationsSimulation and Digital Twins

Topic 3: Distributed AI for autonomous systems (UxVs) with for coordinated swarm operations

The objective of Topic 3 is to explore and demonstrate how autonomous systems powered by machine learning and distributed AI can support coordinated multi-UxV operations in complex operational environments. Experiments funded under this topic should investigate approaches enabling distributed coordination, adaptive task execution, resilient communication, and collaborative swarm behaviours while ensuring human supervision and operational control.

Keywords:Human-in-the-loop SystemsUnmanned Vehicles Autonomous SystemsSwarm Operations