18 Apr 2024

Artificial Intelligence (AI)–Enabled Wargaming Agent Training

Abstract: Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.

17 Apr 2024

Enabling Understanding of Artificial Intelligence (AI) Agent Wargaming Decisions through Visualizations

Abstract: The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a maritime scenario where the AI agent credibly competes against blue agents in gameplay. However, a limitation of using DRL for agent training relates to the transparency of how the AI agent makes decisions. If leaders were to rely on AI agents for COA development or analysis, they would want to understand those decisions. In or-der to support increased understanding, researchers engaged with stakeholders to determine visualization requirements and developed initial prototypes for stakeholder feedback in order to support increased understanding of AI-generated decisions and recommendations. This report describes the prototype visualizations developed to support the use case of a mission planner and an AI agent trainer. The prototypes include training results charts, heat map visualizations of agent paths, weight matrix visualizations, and ablation testing graphs.

29 Feb 2024

Analysis of Vegetation as Terrain: The “How” and “Why” of US Army Doctrine

Abstract: There is a significant knowledge gap for Army doctrine concerning civilian research scientists. A relatively small number of soldiers make the transition from warfighter to research and development at the basic and applied levels. That number is even less when considering former warfighters that have applied Army doctrine in an operational or advanced Army schooling environment. This special report is intended to focus solely on the Army’s current capabilities and doctrinally defined processes to analyze vegetation as an essential component of the natural terrain. The objective of this report is to review current Army doctrine related to analysis of the vegetated terrain; to explore currently leveraged tactics, techniques, and procedures (TTPs); and identify valuable geospatial resources as they apply to military planning. For ease to readers unfamiliar with US Army doctrine, much of the referenced material is directly presented herein as tables and figures throughout the document and appendices (e.g., data sources, product examples, and glossary).

21 Nov 2023

Rotorcraft Resupply Site Selection (RRSS) v1.0 and the USACE Model Interface Platform (UMIP): Documentation and User’s Guide

Abstract: This research effort aimed to create an operational prototype of the Geomorphic Oscillation Assessment Tool (GOAT) v1.0, developed by the US Army Engineer Research and Development Center, as a part of the US Army Corps of Engineers’ Model Interface Platform (UMIP). This platform is a web-based software that allows for easy and rapid construction and deployment of spatial planning and analysis capabilities. The prototype tool in UMIP represents the science embedded in GOAT while providing a user-friendly interface for interaction and spatially referenced result viewing. It also includes user access control, data storage, and integration with a long-term data management system, enabling users to access, share, and interrogate past analyses through profile management and result persistence. The prototype tool incorporates surface roughness into terrain suitability assessment tools used in the forward arming and refueling point (FARP) site-selection process.

25 Jul 2023

Bridge Resource Inventory Database for Gap Emplacement Selection (BRIDGES)

Abstract: Wet gap crossings are one of the most complex maneuvers undertaken by military engineers, who, along with engineer planners, require better tools to increase the capacity for efficient use of limited bridging resources across the battlespace. Planning for bridging maneuvers often involves a complicated and inefficient system of ad hoc spreadsheets combined with an overreliance on the personal experience and training of subject matter experts (SMEs). Bridge Resource Inventory Database for Gap Emplacement Selection (BRIDGES) uses interactive mapping and database technology in order to streamline the bridging planning process and provide answers to question about myriad scenarios to maximize efficiency and provide better means of data persistence across time and data sharing across operational or planning units.

6 Feb 2023

Three-Dimensional Geospatial Product Generation from Tactical Sources, Co-Registration Assessment, and Considerations

Abstract: According to Army Multi-Domain Operations (MDO) doctrine, generating timely, accurate, and exploitable geospatial products from tactical platforms is a critical capability to meet threats. The US Army Corps of Engineers, Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL) is carrying out 6.2 research to facilitate the creation of three-dimensional (3D) products from tactical sensors to include full-motion video, framing cameras, and sensors integrated on small Unmanned Aerial Systems (sUAS). This report describes an ERDC-GRL processing pipeline comprising custom code, open-source software, and commercial off-the-shelf (COTS) tools to geospatially rectify tactical imagery to authoritative foundation sources. Four datasets from different sensors and locations were processed against National Geospatial-Intelligence Agency–supplied foundation data. Results showed that the co-registration of tactical drone data to reference foundation varied from 0.34 m to 0.75 m, exceeding the accuracy objective of 1 m described in briefings presented to Army Futures Command (AFC) and the Assistant Security of the Army for Acquisition, Logistics and Technology (ASA(ALT)). A discussion summarizes the results, describes steps to address processing gaps, and considers future efforts to optimize the pipeline for generation of geospatial data for specific end-user devices and tactical applications.