ARMY AL&T
FIGURE 1 A COMMON ONTOLOGY
As the basis for an AI reference architecture, PEO C3T is considering a design pattern (see Figure 1) to reduce stovepiped elements and rely on a shared data source that will alleviate data flow bottlenecks in an environ- ment with denied, degraded, intermittent or limited connectivity. An information extractor pulls in data from an external system into a shared data resource, or knowledge base. The knowledge base stores data, including both static data like system performance specifications that don’t change, and dynamic data like weather or the safety of a given route. Each reasoner is an algorithm that makes logical inferences about data; it reads data from the knowledge base, processes it and then pushes it back into the knowledge base where it can be consumed by any other reasoner. Finally, an information encoder makes data available in any format required by an external system. A “data fabric” may be used to connect the data of all systems in the portfo- lio, potentially providing the information extraction and encoding features.
MAPPING ON PAPER
PEO C3T’s potential design pattern for an AI reference architecture. (Image courtesy of PEO C3T)
of them are from the training environment, which may not fully represent conditions in the tactical environment. We need data- sharing agreements that span Army or DOD tactical operations. Currently, orders, communications and other types of content are owned by the combatant commands, and are thus classified with a need to know, limiting access for research and development. In addition to providing an opportunity to investigate, document and bridge these gaps, the pathfinder project will partner across Army organizations, leveraging ongoing efforts, to produce useful AI-enabled operational capabilities for Soldiers and leave behind new or enhanced development processes and infrastructure for future AI projects.
Te PEO C3T AI pathfinder project takes a risk management approach to implementing AI infrastructure and functions in mission command applications, acknowledging the uncertainty in acquiring these capabilities in an acquisition system that was designed for tanks and bullets, and working with partners
The common data model that these software components share can be expressed as an ontology— a semantic model of data that describes how the data are related to one another. The ontology provides a unified basis for reasoners, so they no longer require specialized point-to-point data translators. For exam- ple, if a squad is required to travel 100 kilometers to its next destination in a vehicle that has a fuel economy of 10 kilometers per gallon, but the squad has only eight gallons of fuel remaining, a reasoner might identify that the squad does not have sufficient fuel to complete its mission. One vendor could develop a reasoner to vali- date mission resources and push the results into the knowledge base, and another vendor could read in that data to support course-of-action analysis.
By pushing data into a common data store and organiz- ing it according to a common ontology, the information is always available to immediately support any new concept that a developer can dream up—without requiring months of additional software integration. While many applications will need this, there is no prec- edent for building and maintaining an ontology that is used by more than one system—it’s not in anyone’s mission, nor in their funding—but it will be absolutely necessary to sustain anything beyond a very narrow AI in the field.
https://asc.ar my.mil
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