Extending Cognitive Assistance with AI Courses of Action

NPS NRP Executive SummaryThe objectives of this study is to research and assess the initial stages of the evolution of Human-Machine Teaming (HMT) mission workflows which is focused on transitioning of automation tasks from humans to machines using a technique to digitize mission workflows. Also, study the advanced stage(s) of the evolution of HMT to include Courses-of-Action (COA) in Wargaming and how decision-making (DM) AI functions play what role natural language processing (NLP) plays. In addition, this study will explore the viability of NLP in HMT peer-to-peer COAs generation. Finally, this study will leverage complex Joint Naval Force EABO scenario (UNCLASS) designed by MCWL to explore NLP and distributed agents managing the decision making of operators using various modes of HMT interface of AI run-time execution agents thereby enriching digital workflows. The research questions that will be address will include: 1) What is the best approach for a cognitive assistant to learn mission workflows so that recommendations can be made to a human operator?, 2) How can cognitive assistants switch between modes of automatic, advisory, or monitoring?, 3) What are the key parameters for switching?, 4) How does the CA learn to switch to make appropriate recommendations?, 4) What is the cognitive intersection between domain specific environment awareness and situation awareness?, and 5) What happens when a target switches context? The methodology will use quantitative research methods. The methodology for this study will be based on SME input to gain an understanding of mission workflows and tasks, MCWL-developed Joint Force EABO scenario leveraged for a case study and collaboration with the Wargaming Center in Quantico, VA. Based on a scenario, the independent variables will be the inputs into the cognitive assistant. The dependent variable(s) are the output of the system such as if the system recommends the role of automatic, advisory, or monitoring. The plan for this study is to leverage a complex joint Naval Force EABO scenario in studying a role of enrichment digitization of the workflows including utilization of scenario-driven HMT modes and sub-modes; review digital workflows from Master Thesis: "Fire Support Coordination Cognitive Assistant", USMC Capt. Benjamin Herbold, NPS, Graduation Year: June 2020; gain understanding of wargaming COA Digital Mission Command Joint Forces hypergame; develop expertise on modes of Human-Machine Teaming control and their sub-modes of automatic, advisory, and monitoring; study evolution from a single "interactive" mode of HMT proposed for the Fire Support Coordination Digital Workflows to the planning phase in Fire Support Coordination; study NLP and associated theories as a framework to situate the research; and coordinate with other entities such as MIT LL, DARPA, BAE, USMC AI COI, MCWL, and ONR.HQMC Plans, Policies & Operations (PP&O)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Extending Cognitive Assistance with AI Courses of Action

NPS NRP Project PosterThe objectives of this study is to research and assess the initial stages of the evolution of Human-Machine Teaming (HMT) mission workflows which is focused on transitioning of automation tasks from humans to machines using a technique to digitize mission workflows. Also, study the advanced stage(s) of the evolution of HMT to include Courses-of-Action (COA) in Wargaming and how decision-making (DM) AI functions play what role natural language processing (NLP) plays. In addition, this study will explore the viability of NLP in HMT peer-to-peer COAs generation. Finally, this study will leverage complex Joint Naval Force EABO scenario (UNCLASS) designed by MCWL to explore NLP and distributed agents managing the decision making of operators using various modes of HMT interface of AI run-time execution agents thereby enriching digital workflows. The research questions that will be address will include: 1) What is the best approach for a cognitive assistant to learn mission workflows so that recommendations can be made to a human operator?, 2) How can cognitive assistants switch between modes of automatic, advisory, or monitoring?, 3) What are the key parameters for switching?, 4) How does the CA learn to switch to make appropriate recommendations?, 4) What is the cognitive intersection between domain specific environment awareness and situation awareness?, and 5) What happens when a target switches context? The methodology will use quantitative research methods. The methodology for this study will be based on SME input to gain an understanding of mission workflows and tasks, MCWL-developed Joint Force EABO scenario leveraged for a case study and collaboration with the Wargaming Center in Quantico, VA. Based on a scenario, the independent variables will be the inputs into the cognitive assistant. The dependent variable(s) are the output of the system such as if the system recommends the role of automatic, advisory, or monitoring. The plan for this study is to leverage a complex joint Naval Force EABO scenario in studying a role of enrichment digitization of the workflows including utilization of scenario-driven HMT modes and sub-modes; review digital workflows from Master Thesis: "Fire Support Coordination Cognitive Assistant", USMC Capt. Benjamin Herbold, NPS, Graduation Year: June 2020; gain understanding of wargaming COA Digital Mission Command Joint Forces hypergame; develop expertise on modes of Human-Machine Teaming control and their sub-modes of automatic, advisory, and monitoring; study evolution from a single "interactive" mode of HMT proposed for the Fire Support Coordination Digital Workflows to the planning phase in Fire Support Coordination; study NLP and associated theories as a framework to situate the research; and coordinate with other entities such as MIT LL, DARPA, BAE, USMC AI COI, MCWL, and ONR.HQMC Plans, Policies & Operations (PP&O)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Extending Cognitive Assistance with AI Courses of Action

NPS NRP Technical ReportThe objectives of this study is to research and assess the initial stages of the evolution of Human-Machine Teaming (HMT) mission workflows which is focused on transitioning of automation tasks from humans to machines using a technique to digitize mission workflows. Also, study the advanced stage(s) of the evolution of HMT to include Courses-of-Action (COA) in Wargaming and how decision-making (DM) AI functions play what role natural language processing (NLP) plays. In addition, this study will explore the viability of NLP in HMT peer-to-peer COAs generation. Finally, this study will leverage complex Joint Naval Force EABO scenario (UNCLASS) designed by MCWL to explore NLP and distributed agents managing the decision making of operators using various modes of HMT interface of AI run-time execution agents thereby enriching digital workflows. The research questions that will be address will include: 1) What is the best approach for a cognitive assistant to learn mission workflows so that recommendations can be made to a human operator?, 2) How can cognitive assistants switch between modes of automatic, advisory, or monitoring?, 3) What are the key parameters for switching?, 4) How does the CA learn to switch to make appropriate recommendations?, 4) What is the cognitive intersection between domain specific environment awareness and situation awareness?, and 5) What happens when a target switches context? The methodology will use quantitative research methods. The methodology for this study will be based on SME input to gain an understanding of mission workflows and tasks, MCWL-developed Joint Force EABO scenario leveraged for a case study and collaboration with the Wargaming Center in Quantico, VA. Based on a scenario, the independent variables will be the inputs into the cognitive assistant. The dependent variable(s) are the output of the system such as if the system recommends the role of automatic, advisory, or monitoring. The plan for this study is to leverage a complex joint Naval Force EABO scenario in studying a role of enrichment digitization of the workflows including utilization of scenario-driven HMT modes and sub-modes; review digital workflows from Master Thesis: "Fire Support Coordination Cognitive Assistant", USMC Capt. Benjamin Herbold, NPS, Graduation Year: June 2020; gain understanding of wargaming COA Digital Mission Command Joint Forces hypergame; develop expertise on modes of Human-Machine Teaming control and their sub-modes of automatic, advisory, and monitoring; study evolution from a single "interactive" mode of HMT proposed for the Fire Support Coordination Digital Workflows to the planning phase in Fire Support Coordination; study NLP and associated theories as a framework to situate the research; and coordinate with other entities such as MIT LL, DARPA, BAE, USMC AI COI, MCWL, and ONR.HQMC Plans, Policies & Operations (PP&O)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

An analysis of business process re-engineering for government micro-purchasing

This project examines the current business processes for micro-purchases within the government and analyzes the current processes with a potential to be system by utilizing business process re-engineering (BPR). The methodology includes a comparative analysis of BPR methodologies and tools, analysis of the current as is processes for the Naval Postgraduate School (NPS) micro-purchases, and the development of an improved to be processes. Data was gathered from various stakeholders in the purchasing process. BPR software was used to create use cases to study the process flow of the as is and to be systems. The implementation of the process flow, workload, and information systems is highly individual to each agency. The efficiency, effectiveness, and transparency of procurements within individual agencies are highly dependent on leadership, experience, skill sets, training, information technology solutions, and human resources. This research shows working models of improved cost, turn-around-time, and performance. The ultimate goal is to decrease the amount of time that it takes to complete the processes within the workflow system thus improving the turn-around-time for an end user to receive a product or service. Upon completion of the analysis of the as is model and the to be model, savings in both cost and schedule were demonstrated. Re-engineering a few activities that were causing bottlenecks improved the total duration from approximately 20.96 days to 10.4 days. While the changes made are unique to the processes in place at NPS, the structure of BPR can be broadly applied across the government.http://archive.org/details/annalysisofbusin1094543990Civilian, Naval Postgraduate SchoolApproved for public release; distribution is unlimited

Extending CA with AI COAs for Wargaming

NPS NRP Project PosterThe objectives of this study is to research and assess the initial stages of the evolution of Human-Machine Teaming (HMT) mission workflows which is focused on transitioning of automation tasks from humans to machines within the context of Interdependency Analysis; a technique which is used as part of a Co-Active Design in the process of digitization of mission workflows such as Fire Support Coordination (FSCn). Also, to study the advanced stage(s) of the evolution of HMT to include Linguistic Geometry, Real-time Adversarial Intelligence and Decision Making (LG-RAID) Courses-of-Action (COA) Wargaming Decision-making (DM) AI functions and what role natural language processing (NLP) plays. In addition, this study will explore the viability of Interdependence Analysis (IA) matrix and NLP in HMT peer-to-peer COAs generation paradigm as opposed to other approaches. Finally, this study will leverage complex Joint Naval Force EABO scenario (UNCLASS) designed by MCWL to explore NLP and LG-RAID COA's distributed agents managing the decision making of operators using various modes of HMT interface of AI run-time execution agents thereby enriching digital workflows.HQMC Plans, Policies & Operations (PP&O)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Extending CA with AI COAs for Wargaming

NPS NRP Executive SummaryThe objectives of this study is to research and assess the initial stages of the evolution of Human-Machine Teaming (HMT) mission workflows which is focused on transitioning of automation tasks from humans to machines within the context of Interdependency Analysis; a technique which is used as part of a Co-Active Design in the process of digitization of mission workflows such as Fire Support Coordination (FSCn). Also, to study the advanced stage(s) of the evolution of HMT to include Linguistic Geometry, Real-time Adversarial Intelligence and Decision Making (LG-RAID) Courses-of-Action (COA) Wargaming Decision-making (DM) AI functions and what role natural language processing (NLP) plays. In addition, this study will explore the viability of Interdependence Analysis (IA) matrix and NLP in HMT peer-to-peer COAs generation paradigm as opposed to other approaches. Finally, this study will leverage complex Joint Naval Force EABO scenario (UNCLASS) designed by MCWL to explore NLP and LG-RAID COA's distributed agents managing the decision making of operators using various modes of HMT interface of AI run-time execution agents thereby enriching digital workflows.HQMC Plans, Policies & Operations (PP&O)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.