David R. Jacques’s research while affiliated with U.S. Air Force Institute of Technology and other places

Digital Engineering (DE) and Model‐Based Systems Engineering (MBSE) are transforming existing acquisition processes. Little extant research exists regarding validation of digital system models. In the traditional “design‐build‐test” method, stakeholder requirements validation occurs near the end of development. However, DE is described as a “model‐analyze‐build” approach where validation is required earlier in the lifecycle to refine and add digital elements to the authoritative source of truth. This paper describes a framework for model‐based design reviews (MBDR) and extends previous work by Hecht and Chen. Validation use case development is emphasized to capture both the intent, the “why,” and the evidence, the “what,” required to enter or exit a preliminary design review (PDR). Model requirements are derived from model use cases, and validation test cases enable automated and manual assessment of quality, completeness, and consistency. The framework is successfully prototyped on a small unmanned airborne system (sUAS) model created for a graduate course PDR. There appears to be wide interest across the U.S. Air Force for a similar framework.

Knowledge of intent is critical in high performing human teams. The fundamental question addressed by this research is, how should intent be integrated into future human‐artificial agent (AA) teams to improve coordination among team members? A brief review of the use of intent for improving performance within human‐human teams is conducted to provide a better understanding of this term. This review differentiates intent estimation from intent application, as well as the differentiation of “why,” “what” and “how” based intent. A taxonomy of intent‐based systems is then developed through a review of existing examples in the literature. Together these reviews demonstrate that intent has been modeled in a variety of ways without a cohesive understanding of intent and its different forms. Based upon these reviews and our understanding of multi‐agent system architectures, we propose “operationalized intent” as a method of modeling intent regarding “how” the operators would like to execute the team's tasks. We propose including an Intent Agent (IA) dedicated to estimating intent of each operator and embedding knowledge of how to execute within the Functional Agents (FAs) of a multi‐agent system. The proposed Operationalized Intent Ontology provides a means of modeling human‐agent teams as an intent informed system.

Teaming permits cognitively complex work to be rapidly executed by multiple entities. As artificial agents (AAs) participate in increasingly complex cognitive work, they hold the promise of moving beyond tools to becoming effective members of human–agent teams. Coordination has been identified as the critical process that enables effective teams and is required to achieve the vision of tightly coupled teams of humans and AAs. This paper characterizes coordination on the axes of types, content, and cost. This characterization is grounded in the human and AA literature and is evaluated to extract design implications for human–agent teams. These design implications are the mechanisms, moderators, and models employed within human–agent teams, which illuminate potential AA design improvements to support coordination.

There is an ongoing demand for organizations to become more agile in order to prosper amongst their competitors. Many military organizations have declared a renewed focus towards organizational agility. The goal of this research is to isolate the variables needed to measure organizational agility (OA) in military organizations, allowing for the future development of a suitable method to measure OA without the need to interact with outside organizations. This article begins by providing a suitable and formal definition of organizational agility by exploring and analyzing relevant scholarly literature on the subject. Related terms, such as organizational resiliency, flexibility, robustness, versatility, and adaptability are also explored to examine their definition boundaries and any overlapping areas. Existing methods to measure organizational agility are examined and summarized, and the current limitations to their application are highlighted. Previous studies to find characteristics associated with organizational agility were also examined, and an initial set of 88 organizational agility characteristics was built. Since these included possible redundant or overlapping characteristics, the Q-sort method was employed to discover, analyze, and eliminate redundant items from the dataset, ultimately resulting in 64 unique characteristics. The result is a suitable definition for organization agility applicable to military organizations and a list of potential associated characteristics that summarizes related research to date. This groundwork establishes the foundation to conduct a multi-organization study to further refine the characteristic list and ultimately develop a method to measure organizational agility.

Levels of Automation (LOA) provide a method for describing authority granted to automated system elements to make individual decisions. However, these levels are technology-centric and provide little insight into overall system operation. The current research discusses an alternate classification scheme, referred to as the Level of Human Control Abstraction (LHCA). LHCA is an operator-centric framework that classifies a system’s state based on the required operator inputs. The framework consists of five levels, each requiring less granularity of human control: Direct, Augmented, Parametric, Goal-Oriented, and Mission-Capable. An analysis was conducted of several existing systems. This analysis illustrates the presence of each of these levels of control, and many existing systems support system states which facilitate multiple LHCAs. It is suggested that as the granularity of human control is reduced, the level of required human attention and required cognitive resources decreases. Thus, it is suggested that designing systems that permit the user to select among LHCAs during system control may facilitate human-machine teaming and improve the flexibility of the system.