READY is a tool specifically developed for the design of scenarios and strategic action plans for military, public, and private organizations. Situations are created then simulated when confronted with enemies, disasters, or exceptional events, and can be modified iteratively, allowing for the comparison of outcomes.
The project is based on the SWORD simulation, an aggregated and automated constructive multi-agent simulation that uses Direct AI, an artificial intelligence middleware solution designed for the modeling of human and non-human behaviors.
The READY project therefore brings a new type of tool to the market, allowing customers to:
- follow training sessions that teach them the impact of decisions and entire action plans via a simulated world
- design, evaluate, and optimize the strength and resilience of maneuvers, interventions, and rescue plans, by testing them when faced with the demands of conceivable events, enemies, and disasters
- perform operational analysis. For example, would an intended intervention be more effective if more personnel, equipment, or different equipment types were available? If teams failed to combine as expected how would the outcome be affected? Would a plan still be viable if the enemy opted for a radically different tactical approach?
- benefit from planning support by foreseeing future requirements, both in terms of equipment and doctrine. Part of this involves the ability to test the effectiveness and value of prospective weapon systems and equipment.
More formally stated, the goal of READY is to be able to edit ‘Courses of Action’ (COA), – variations on the same scenario -, and to compare versions, both visually by watching the unfolding of several COAs simultaneously, and statistically, via indicators based on numeric analysis.
Easy-to-use, READY can be installed on portable devices in a few simple steps.
Existing tools available in the field of public safety are mainly aimed at helping decision makers to formalize an action plan. Here the proposed solution goes much further, allowing you to not only design the plan, but also to simulate it, then rewind, adjust the hypothesis (more/less material means, wounded, enemies; alternative enemy responses, different types of disaster, etc.), and furthermore to retest for evaluation purposes in a large number of environments.
At an earlier stage in the process, decision makers can use this solution to perform operational analysis in forecast laboratories, or simply for training purposes (tactical theory, action plan design).
Finally, as the simulation is not deterministic, you also have the option of simulating the same scenario repeatedly in order to identify likely variations in the way events unfold.
The ability to compare and analyze simulation results completes the proposal. It is plausible that this facility could in the future be complemented by a machine learning solution for the processing of the large volumes of data generated, thereby optimizing action plans.
To go further still, it is perfectly conceivable to propose the use of this tool in crisis or command cells as a decision-support device that offers a rapid, portable, and realistic simulation of a plan when faced with a situation that bears little resemblance to the classic, expected state of affairs envisaged at conception.