Core Features Power and flexibility
Powerful Agent-Based Modeling Environment
SEAS offers a powerful agent-based modeling environment that allows the analyst to simulate the complex, adaptive interactions of opposing military forces in a physics-based battlespace. Agents (units and platforms) execute programmable behavioral and decision-making rules based on battlespace perception. The interaction of the agents with each other and their environment results in warfighting outcomes, enabling Militay Utililty Analysis (MUA).
Integrated Development Environment for Model Construction
SEAS includes an Integrated Development Environment (IDE) based on the open source Eclipse framework. The IDE provides a single interface for developing, managing, and running SEAS model files. The IDE parses the SEAS TPL syntax in real-time and proposes quick fixes to syntax errors. The SEAS engine can be launched directly from the IDE to simply model testing and improve efficiency of the model development cycle.
Flexibility in the Hands of the Analyst
The programmable and constructive nature of SEAS allows the analyst to create models with varying degrees of fidelity, resolution, sophistication, and complexity. While SEAS is typically used for scenario-based military operations research involving aircraft, satellites, ground vehicles, communications systems, weapons and sensors, its flexibility enables it to be used to analyze a broad range of complex adaptive systems, not just military scenarios.
Analytical Graphics and Visualizations
SEAS produces graphic visualizations as a simulation is running. The user interface provides several different view options for displaying map features, sensors fields, satellite orbits, and output plots as results are generated. The analyst can also completely control the output display programmatically with TPL. This powerful feature enables improved debugging and allows the analyst to convey specific model details and results.
Optimized Performance
Most agent-based simulations have performance limitations, and do not scale well since the number of potential agent interactions increases exponentially as the number of agents increase. SEAS tackles this problem with a rich set of advanced algorithms and techniques for efficient simulation and optimization. The result is a simulation system that offers high performance and scalability, with no special hardware requirements.