Abstract
Sophisticated peer adversaries with greater force numbers, the tyranny of distance, and the complexity of all-domain environments threaten combined/joint operational effectiveness—especially in a standing-start scenario. Countering drives new agile kill-web concepts incorporating autonomous systems and high-speed sensor–to–shooter data flows. Today’s operational complexity juxtaposed with required command and control (C2) speed-of-the-fight decision-making creates unprecedented gaps in capabilities to support strategy-to-task planning, while balancing risk, survivability, and lethality. Additionally, United States Indo-Pacific Command (INDOPACOM) requires the ability for forward deployed forces to train in their Joint Operating Area, without revealing their hand, to preserve combat credibility and achieve desired deterrent effects. Equally important is enabling on-demand training with requisite ‘reps and sets,’ unhindered by exercise schedules. This paper discusses an innovative Live, Virtual, and Constructive (LVC) solution demonstrated at forward-deployed locations via the Pacific Multi-Domain Training and Experimentation Capability (PMTEC) Program Office. There are five facets to this paper.
First, it presents a solution which integrates an Artificial Intelligence enabled engine, constructive off-the-shelf-capabilities, and virtual and live aircraft. Secondly, it discusses the integration with other constructive capabilities, experimentation, and test efforts across multiple iterations of multiple events on operational networks. Thirdly, this paper presents key LVC “firsts,” which enabled the accomplishments of key objectives (e.g., the first Joint Integrated Air and Missile Defense of Guam mission rehearsal. Fourthly, this paper addresses initial lessons learned and recommendations on developing and employing LVC in the INDOPACOM Area of Responsibility. Finally, this paper will touch on a next steps 5D Chess concept of taking ‘a posteriori’ observations and applying them to new iterations as synthetic ‘a priori’ knowledge to improve plans (i.e., how new constraint identification or a key sequel or branch plan can effectively be moved back in the timeline or to adjacent parallel planning activities being run by the engine.