SBIR-STTR Award

Current course of action (COA) planning is predominantly a manual ad hoc process where the mission planner seeks to maximize the immediate effects of his COAs while reacting to the opposition's known COAs, and possibly countering the enemy's immediate anticipated response. Long-term moves and countermoves are not considered, nor are objectives achieved in a calibrated quantitative manner. There is a need to search for optimal COAs by considering multiple friendly COAs in light of multiple potential enemy COAs (eCOAs) and "dovetailing" the effects of each side's actions into a chain of moves and countermoves, actions and reactions, that defines the overall sequence of unfolding events in combat. This Phase I project will develop and test software that uses an upgraded JANUS simulation to address this need within the context of UAV (or other vehicles) mission planning in light of enemy defenses. The Phase II effort will treat scenarios involving scores to hundreds of units on multiple sides, and be demonstrated within an EBO context. The software will be applicable to all branches of the military, and to the software entertainment industry. Natural Selection, Inc.'s sister company, Digenetics, Inc., is already well positioned to effect a successful commercial transition.

Keywords:
Course Of Action (Coa), Decision Making, Command And Control, Evolutionary Computation, Valuated State Space(R), Gaming, Video Games

There is a critical need for combat simulations that incorporate intelligently interactive agents, both for mission planning and training. Advances in simulation now offer the opportunity to choose simulation as an effective and cost-saving means for training; however, to be most effective, the agents in the simulation (e.g., an OPFOR) must act believably, in light of a quantified mission/purpose, and choose their courses of action (COAs) based on the likely opposing force’s response (eCOAs), extended as a chain of moves and countermoves. Phase I has demonstrated an innovative COA-eCOA process that uses evolutionary algorithms to optimize COA-eCOA decisions. Experiments involving UCAVs and mobile SAMs have documented the run-time performance of the process. Missions are described in a Valuated State Space® and normalizing function, which provides a framework that can incorporate specific target prioritization, timeliness of sensing and attacks, effects-based operations, and so forth. The work to date has set the stage for the Phase II that will construct the necessary hardware architecture, software development and optimizations, experimentation, and that can be brought to fruition in both government and civilian applications, including mission planning and training for all branches of the military, and entertainment software in the private sector.

Keywords:
COURSE OF ACTION, DYNAMIC PLANNING, REPLANNING, EV