Reverse Engineering of Computer-Based Navy Systems

The financial pressure to meet the need for change in computer-based systems through evolution rather than through revolution has spawned the discipline of reengineering. One driving factor of reengineering is that it is increasingly becoming the case that enhanced requirements placed on computer-based systems are overstressing the processing resources of the systems. Thus, the distribution of processing load over highly parallel and distributed hardware architectures has become part of the reengineering process for computer-based Navy systems. This paper presents an intermediate representation (IR) for capturing features of computer-based systems to enable reengineering for concurrency. A novel feature of the IR is that it incorporates the mission critical software architecture, a view that enables information to be captured at five levels of granularity: the element/program level, the task level, the module/class/package level, the method/procedure level, and the statement/instruction level. An approach to reverse engineering is presented, in which the IR is captured, and is analyzed to identify potential concurrency. Thus, the paper defines concurrency metrics to guide the reengineering tasks of identifying, enhancing, and assessing concurrency, and for performing partitioning and assignment. Concurrency metrics are defined at several tiers of the mission critical software architecture. In addition to contributing an approach to reverse engineering for computer-based systems, the paper also discusses a reverse engineering analysis toolset that constructs and displays the IR and the concurrency metrics for Ada programs. Additionally, the paper contains a discussion of the context of our reengineering efforts within the United States Navy, by describing two reengineering projects focused on sussystems of the AEGIS Weapon System

An Optimization Approach to Balancing Risk and Cost in Combatant Command Capability Advocacy

Unified Combatant Commands (UCCs) have broad continuing missions around the globe where they are tasked to provide functional expertise and defense of geographical areas.  Accomplishing these missions requires a robust portfolio of military capabilities (e.g., aircraft, spacecraft, command and control systems, radar systems).  UCCs routinely perform analyses to identify gaps between capabilities required to accomplish their mission and those currently at their disposal.  Each year they submit a prioritized list of required capabilities, including new systems and greater capacity with existing systems, to the Joint Staff in the costly and time-consuming Integrated Priority List (IPL) process.  This process relies on operational art and subject matter expertise, and sometimes fails to identify acquisition opportunities that achieve an optimal balance between risk and cost.  Because this IPL process affects all of the DOD’s personnel, material, systems and missions, it is arguably the most significant analytic challenge faced by the United States military.  This article presents an integer linear programming model that computes an optimal balance between operational risk and the cost of acquiring new capabilities, and allows decision makers to identify the real-world impact of their budgetary decisions.  We apply this model to the mission of providing aerospace defense of the United States and illustrate through sensitivity analysis the meaningful insights that can be gained by studying the relationship between the risk of not achieving 100 percent radar coverage and the opportunity cost of advocating for new capabilities

Unconventional Methods for a Traditional Setting: The Use of Virtual Reality to Reduce Implicit Racial Bias in the Courtroom

The presumption of innocence and the right to a fair trial lie at the core of the United States justice system. While existing rules and practices serve to uphold these principles, the administration of justice is significantly compromised by a covert but influential factor: namely, implicit racial biases. These biases can lead to automatic associations between race and guilt, as well as impact the way in which judges and jurors interpret information throughout a trial. Despite the well-documented presence of implicit racial biases, few steps have been taken to ameliorate the problem in the courtroom setting. This Article discusses the potential of virtual reality to reduce these biases among judges and jurors. Through analyzing the various ethical and legal considerations, this Article contends that implementing virtual reality training with judges and jurors would be justifiable and advisable should effective means become available. Given that implicit racial biases can seriously undermine the fairness of the justice system, this Article ultimately asserts that unconventional de-biasing methods warrant legitimate attention and consideration

A methodology for the quantification of doctrine and materiel approaches in a capability-based assessment

Due to the complexities of modern military operations and the technologies employed on today's military systems, acquisition costs and development times are becoming increasingly large. Meanwhile, the transformation of the global security environment is driving the U.S. military's own transformation. In order to meet the required capabilities of the next generation without buying prohibitively costly new systems, it is necessary for the military to evolve across the spectrum of doctrine, organization, training, materiel, leadership and education, personnel, and facilities (DOTMLPF). However, the methods for analyzing DOTMLPF approaches within the early acquisition phase of a capability-based assessment (CBA) are not as well established as the traditional technology design techniques. This makes it difficult for decision makers to decide if investments should be made in materiel or non-materiel solutions. This research develops an agent-based constructive simulation to quantitatively assess doctrine alongside materiel approaches. Additionally, life-cycle cost techniques are provided to enable a cost-effectiveness trade. These techniques are wrapped together in a decision-making environment that brings crucial information forward so informed and appropriate acquisition choices can be made. The methodology is tested on a future unmanned aerial vehicle design problem. Through the implementation of this quantitative methodology on the proof-of-concept study, it is shown that doctrinal changes including fleet composition, asset allocation, and patrol pattern were capable of dramatic improvements in system effectiveness at a much lower cost than the incorporation of candidate technologies. Additionally, this methodology was able to quantify the precise nature of strong doctrine-doctrine and doctrine-technology interactions which have been observed only qualitatively throughout military history. This dissertation outlines the methodology and demonstrates how potential approaches to capability-gaps can be identified with respect to effectiveness, cost, and time. When implemented, this methodology offers the opportunity to achieve system capabilities in a new way, improve the design of acquisition programs, and field the right combination of ways and means to address future challenges to national security.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Bishop, Carlee; Committee Member: Brown, David; Committee Member: Costello, Mark; Committee Member: Schrage, Danie

Autonomy in Weapons Systems. The Military Application of Artificial Intelligence as a Litmus Test for Germany’s New Foreign and Security Policy

The future international security landscape will be critically impacted by the military use of artificial intelligence (AI) and robotics. With the advent of autonomous weapon systems (AWS) and a currently unfolding transformation of warfare, we have reached a turning point and are facing a number of grave new legal, ethical and political concerns. In light of this, the Task Force on Disruptive Technologies and 21st Century Warfare, deployed by the Heinrich Böll Foundation, argues that meaningful human control over weapon systems and the use of force must be retained. In their report, the task force authors offer recommendations to the German government and the German armed forces to that effect

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications