COMMENTARY
can express a taxonomy, outlining that a brigade contains battalions and a battal- ion contains companies; they can also define that a brigade is led by a colonel, or a squad contains between four and 10 Soldiers. Tis structure can be expanded to describe equipment and supplies, the capability of weapons, characteristics of maneuver and more.
Identifying the potential effect of isolated weapons on a particular target is a cumber- some task akin to delivering a series of rocks—“Is this it?” Each weapon-target pairing is applied in series to determine the outcome. While this approach could be aided with tabulated look-up tables, deductive reasoning can provide solu- tions, often involving multiple weapons or tactics, such as timing weapons in a series, to deliver a desired effect on a spec- ified target. With a structure in place to provide the logic, an algorithm can deter- mine which units have weapons with the desired effect on a selected target, as well as their ability to maneuver as necessary, given the speed of their vehicles and the state of their supplies.
IS IT DEDUCTIBLE? Deductive reasoning is being applied to analysis of suspected chemical or biologi- cal laboratories. One could approach this problem by making an exhaustive list of materials and equipment used in the production of various substances and then comparing those lists to what is found in a given facility, but it’s difficult to be comprehensive.
A gas burner and a beaker might be the laboratory standard for heating a liquid, but if we look for those specific elements, we might miss the more common case of a heat source like a fire and a container capable of holding hot liquids, like a metal pot. An ontology might specify that a pot has the capability of holding liquid and is
The need for a broader AI tool was highlighted by the Army National Guard’s efforts to support the national COVID-19 response.
composed of steel, while steel has the prop- erty of a high melting point, which allows heat to be applied to raise the temperature of a liquid contained in the pot.
Tis might seem intuitive to a human based on learned experience, but it is a fairly complex concept for a computer, though one apparently not lost to Arnold Schwarzenegger’s Model 101 terminator at the end of the second movie when he lowers himself into a vat of molten metal.
CONCLUSION AI has long been a staple of science fiction, allowing mechanical beings to interact with humans on more or less equal terms. In the dystopian tradition, technology is often presented as a menace, as in the “Terminator” series, or HAL from “2001: A Space Odyssey,” but there are also more positive AI role models in popular culture, such as C-3PO from “Star Wars” or Tony Stark’s “Iron Man” interface, Just A Rather Very Intelligent System (JARVIS). One might imagine similar technology supporting our Soldiers, warning them of incoming threats and plotting oppor- tunities through data-informed course of action analyses.
Te Army Data Strategy includes a goal for making data understandable by users, but this should be expanded from human users to AI agents as well. Providing an ontology as a structure for machine understand- ing is essential for future AI applications. JARVIS embodies many of the capabilities
the Army seeks to enable with AI, for example: automatic speech recognition combined with natural language process- ing, visual entity extraction and automatic target recognition, health monitoring and damage assessment. Absent a knowledge structure for machine reasoning, many of these capabilities cannot be realized.
Another goal of the Army Data Strategy is for data to be interoperable across systems. Tis is supported by data exchange stan- dards, but adoption of NIEM and an ontology framework like Basic Formal Ontology (BFO) are not mutually exclu- sive. One can be used to supplement the other, and both use universal resource indicators to unambiguously identify data elements. Te development of conceptual domain ontologies is necessary for reason- ing to span domains such as medicine and logistics, or fires and command-and- control, where the same data element may have different types of relationships. Because ontologies are extensible, allow- ing data elements to have different types of relationships, domains can be developed independently to an extent, but should still be governed by an umbrella framework such as BFO, to ensure that those relation- ships are defined in a consistent manner.
The potential for computer reason-
ing to advance the Army’s ability to get supplies to hospitals at the time of need, or optimizing battle plans, represents a transformative future for our Army and our military.
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