MP-MSG-229-11.pdf — page 5

Orchestrating Manned-Unmanned Missions 
Using AI-Based Tasking System Across Multi-Domain Operations in M&S 
STO-MP-MSG-229 
11 - 5 
 
 
analogy in unmanned agents, although system errors or failures can impact performance. Leadership and 
intent in humans involve ethical judgment, motivation, and context-aware decisions, whereas unmanned 
systems rely on hard-coded leadership structures or programmed goals, with intent often inferred for 
coordination purposes. These differences highlight the need to carefully model both human variability and 
unmanned system capabilities when simulating MUM-T operations for force readiness. Thus, challenges 
might rise due to the major differences found among MUM-T teams as listed in Table 1. This table is based 
on [3] and has been augmented and revised here.  
1.1.4 
AI for Decision-Support in MUM-T  
The use of AI has gained interest on the battlefield on the tactical level. At both the operational and strategic 
tiers of military operations, artificial intelligence significantly supports data analysis and planning efforts. Its 
capacity to handle vast amounts of information enables AI to assist in decisions such as force deployment 
and selecting plans that most effectively advance strategic objectives [14]. This suggests considerable 
potential for AI to play a larger role in high-level military decision-making in the future [14]. While AI is not 
yet widely deployed in this capacity, certain systems have demonstrated superior performance compared to 
human military personnel at the tactical level, indicating strong potential for expanded AI involvement in 
strategic military decision-making in the future. AI enables the synthesis of a large amount of data in short 
amount of time, which might help counter human biological limitations. These systems employ AI-driven 
targeting and control mechanisms capable of processing information and executing actions within fractions 
of a second [14]. AI could similarly be applied to decision-support when it comes to counter rapidly 
emerging threats, such as hypersonic missiles, where the response window is extremely limited, although no 
such defenses are currently known to exist. The growing presence of autonomous weapons on the battlefield 
highlights AI’s tactical significance, as its speed and responsiveness in dynamic combat scenarios can 
provide a decisive advantage over conventional systems. At higher levels of warfare, including operational 
and strategic planning, AI contributes substantially to data analysis and decision support. Its ability to 
process large volumes of information allows it to inform choices such as force deployment and selection of 
plans that best advance strategic objectives. It can help solve difficult questions such as “where to commit 
forces, what plan would get the closest to the strategic objective and more [14]. Thus, this can help support 
the MDMP during mission to re-adjust COA based on constant data streaming, dynamic changes in MUM-T 
capabilities, and situational awareness.  
2.0 STAR (SHARING TASKS WITH AUTONOMOUS RESSOURCES) 
STAR is designed in order to facilitate the OODA loop for decision-making in the three phases of missions: 
i) Mission pre-operations - via supporting war gaming simulation and determination the best course of action 
based on if-then scenario, ii) in-operation - as it can help support decision-making surrounding dynamic 
tasking since often plans and orders given prior to the mission might change due to change of events, iii) 
STAR could also help support after-action analysis to better prepare and support future mission, help assess 
current states of each agent capability to perform their tasks and duties, it could be used to calibrate training / 
performance of human and autonomous agent through modelling and simulation. STAR could facilitate the 
readiness of forces by providing a system capable of plugging into a simulator which can help generate and 
put in place mission operations, launch team, and test and evaluate different strategies while receiving AI 
recommendation towards tasking of MUM-T. STAR is composed of several components which are 
described in Section 2.1. The three main modules presented are i) multi-modal pipeline; ii) capability 
reckoning, iii) dynamic tasking module. Each of those three can be fed from simulation and modelling data 
which enables the system to be tested on specific use-cases which are of interest and can help gather data and 
potential behavioral insight towards how each manned or unmanned agent might behave in the mission.