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.

26 Mar 2024

Risk-Based Prioritization of Operational Condition Assessments: Trinity River and Willamette River Case Studies

Abstract: The US Army Corps of Engineers (USACE) operates, maintains, and man-ages over 700 dams and 4,000 miles of levees, providing approximately $257 billion worth of economic benefit to the Nation. USACE employs the Operational Condition Assessment (OCA) process to understand the condition of those assets and allocate resources to minimize risk associated with performance degradation. Understanding risk in flood risk management (FRM) assets requires an understanding of consequence of asset failure from a systemwide FRM watershed perspective and an understanding of likelihood of degradation based on the condition of the low-level components derived from OCA ratings. This research demonstrates a case-study application of a scalable methodology to model the likelihood of a dam performing as expected given the state of its gates and their components. The research team combines this likelihood of degradation with consequences generated by the application of designed simulation experiments with hydrological models to develop risk measures. These risk measures can be developed for all FRM gate assets in order to enable traceable, consistent resource allocation decisions. Two case study applications are provided.

11 Mar 2024

The Madness behind the Method: Showing the Human Side of Developing a World-Class Institution

Abstract: The 2022–2023 Leadership Development Program 2 (LDP2) team from the Information Technology Laboratory (ITL) created this document to explore the human side of ITL’s history through the viewpoints of former influential figures at the lab. These individuals played a crucial role in elevating the lab to its current prominent position. The dynamic nature of the document allows continuous addition of such stories, providing future generations with insight into the unwavering commitment of the pioneers who established ITL’s esteemed legacy. Each nar-rative sheds light on different aspects of the lab, including its people, diversity, and excellence. The document serves as both a tribute to the lab’s achievements and a wellspring of inspiration for aspiring leaders, showcasing the profound impact of dedication and teamwork. By cele-brating these stories, we are able to learn from those who came before us and cultivate an enhanced vision for the future.

16 Feb 2024

A Comprehensive Review on Wood Chip Moisture Content Assessment and Prediction

Abstract: Wood chips are the primary sources of raw materials for numerous industries, including pelleting mills, biorefineries, pulp-and-paper industries, and biomass-based power generation facilities. Unfortunately, when wood chips are utilized as a renewable and environmentally friendly resource, industries are constantly challenged by the consistency of the wood chip qualities (e.g., moisture/ash contents, size distributions) - a historically recognized problem on a global scale. Among other wood chip quality attributes, the moisture content is considered the most pressing one as it directly impacts the energy content, storage stability, and handling properties of the raw and finished products. Therefore, accurate wood chip moisture content prediction can help optimize the drying process and reduce energy consumption. In this review, a survey was conducted on various techniques and models employed for predicting wood chip moisture content. The advantages and limitations of these approaches, as well as their potential applications and future directions were also discussed. This review aims to provide a comprehensive overview of the current state-of-the-art in wood chip moisture content prediction and to highlight the challenges and opportunities for further research and development in this field.

8 Jan 2024

RISC TAMER Framework: Resilient Installation Support Against Compound Threats Analysis and Mitigation for Equipment and Resources Framework

Every day, decision-makers must allocate resources based on the best available information at the time. Military installations face a variety of threats which challenge sustained functionality of their supporting and supported deployable systems. Considering the compounding and interdependent impacts of the threats, both specified (what is known) and unspecified (what is not known) and the investments needed to address these threats adds value to the decision-making process. Current risk management practices are generally evaluated via scenario analyses that do not consider compound threats, resulting in limited risk management solutions. Current practices also challenge the ability of decision-makers to increase resilience against such threats. The Resilient Installation Support against Compound Threats Analysis and Mitigation for Equipment and Resources (RISC TAMER) Framework establishes a decision support structure to identify and categorize system components, compound threats and risks, and system relationships to provide decision-makers with more complete and comprehensive information from which to base resilience-related decisions, for prevention and response. This paper focuses on the development process for RISC TAMER framework to optimize resilience enhancements for a wide variety of deployable systems in order to implement resilience strategies to protect assets, to increase adaptability, and to support power projection and global operations.

4 Jan 2024

Dockerization of the Coastal Model Test Bed Toolkit

Purpose: The purpose of this technical note is to document and describe changes made to the Coastal Model Test Bed (CMTB) suite of software in conjunction with the version 2 (V2) update.

4 Jan 2024

ERDC-PT: A Multidimensional Particle Tracking Model

Abstract: This report describes the technical engine details of the particle- and species-tracking software ERDC-PT. The development of ERDC-PT leveraged a legacy ERDC tracking model, “PT123,” developed by a civil works basic research project titled “Efficient Resolution of Complex Transport Phenomena Using Eulerian-Lagrangian Techniques” and in part by the System-Wide Water Resources Program. Given hydrodynamic velocities, ERDC-PT can track thousands of massless particles on 2D and 3D unstructured or converted structured meshes through distributed processing. At the time of this report, ERDC-PT supports triangular elements in 2D and tetrahedral elements in 3D. First-, second-, and fourth-order Runge-Kutta time integration methods are included in ERDC-PT to solve the ordinary differential equations describing the motion of particles. An element-by-element tracking algorithm is used for efficient particle tracking over the mesh. ERDC-PT tracks particles along the closed and free surface boundaries by velocity projection and stops tracking when a particle encounters the open boundary. In addition to passive particles, ERDC-PT can transport behavioral species, such as oyster larvae. This report is the first report of the series describing the technical details of the tracking engine. It details the governing equation and numerical approaching associated with ERDC-PT Version 1.0 contents.

29 Nov 2023

Toxic Industrial Chemical / Material Intelligence Tool (TICMINT) User Guide

Abstract: The Toxic Industrial Chemical / Material Intelligence Tool (TICMINT) is a web application that provides critical chemical and toxicological information to users quickly and efficiently for the purpose of enacting safe maneuvers in areas of operations. It provides an in-depth look at the makeup, properties, and hazardous effects of nearly 400 toxic chemicals of interest. It also provides background on the chemical makeup of a bevy of building materials, enabling soldiers in areas of operation to determine the toxicological risks associated with the combustion of those materials in their environment. This document’s purpose is to demonstrate the functionality of the TICMINT web application and provide instructional material for those managing its content.

28 Nov 2023

Discover ERDC 101 and 201 Training Modules User’s Guide

Abstract: Discover ERDC is a web-based tool that functions as a knowledge management hub by enabling employees of the US Army Engineer Research and Development Center (ERDC) to access valuable resources such as detailed employee profiles, organizational details, and links to other knowledge stores. This document covers the update of the ERDC 101 and 201 video player systems, the addition of a training component to those modules, and the integration of the systems into Discover ERDC. The updated video systems contain a collection of onboarding video presentations that give new employees critical information about their careers at ERDC. In addition, Discover ERDC 101 and 201 provide progress-tracking mechanics for asynchronous learning, as well as the ability to certify that employees have completed the training modules. This document serves as a user guide for these tools, providing an overview of the content and functionality.