Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Technical ReportThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis 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.

Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Executive SummaryThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis 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.

Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Project PosterThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis 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.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis 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.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Project PosterThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis 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.

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Executive SummaryThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis 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.

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Executive SummaryThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis 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.

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Technical ReportThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis 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.

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Project PosterThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis 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.

Considerations for Cross Domain / Mission Resource Allocation and Replanning

NPS NRP Executive SummaryNaval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources.N2/N6 - Information WarfareThis 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.