Game Theory and U-Boats in the Bay of Biscay

Analysis of a large combat campaign using game theory is difficult due to non- linearities and other soft factors which exist in a complex system. However, game theory can give decision makers insight into strategies and outcomes that can be utilized to maximize one\u27s objective. Agent-based simulation provides the means to model complex systems with non-linearities, by allowing for interactions among independent agents. This thesis investigates game-theoretic strategies in agent-based simulation, modeled after the Allied search for U-boats in the Bay of Biscay during World War II (WWII). It also looks into the effects of adaptation on strategies by comparison to fixed-strategy results

Application of a Multi-Objective Network Model to a Combat Simulation Game: The Drive on Metz Case Study

War games are routinely analyzed by the Department of Defense to study the players decision making process. This research develops a multicriteria model that enhances a war game players decision-making capability. The war game consists of a hexagonal-grid map of varying terrain that will be represent as a two-dimensional directed network. The network is obstructed by multiple enemy threats that expose a unit traversing the network to possible attack. The player is faced with the decision of choosing a route to a target node that balances the objectives of following the shortest path and maximizing the probability of success. A weighted arc cost matrix is supplied to Dijkstras shortest path algorithm to and an optimal route. Critical values of the ratio of the objective function weights determine where the optimal path changes. These values are determined on a test scenario for the war game The Drive On Metz

Cooperative Wide Area Search Algorithm Analysis Using Sub-Region Techniques

Recent advances in small Unmmaned Aerial Vehicle (UAV) technology reinvigorates the need for additional research into Wide Area Search (WAS) algorithms for civilian and military applications. But due to the extremely large variability in UAV environments and design, Digital Engineering (DE) is utilized to reduce the time, cost, and energy required to advance this technology. DE also allows rapid design and evaluation of autonomous systems which utilize and support WAS algorithms. Modern WAS algorithms can be broadly classified into decision-based algorithms, statistical algorithms, and Artificial Intelligence (AI)/Machine Learning (ML) algorithms. This research continues on the work by Hatzinger and Gertsman by creating a decision-based algorithm which subdivides the search region into sub-regions known as cells, decides an optimal next cell to search, and distributes the results of the search to other cooperative search assets. Each cooperative search asset would store the following four crucial arrays in order to decide which cell to search: current estimated target density of each cell; the current number of assets in a cell; each cooperative asset’s next cell to search; and the total time any asset has been in a cell. A software-based simulation based environment, Advanced Framework for Simulation, Integration, and Modeling (AFSIM), was utilized to complete the verification process, create the test environment, and the System under Test (SUT). Additionally, the algorithm was tested against threats of various distributions to simulate clustering of targets. Finally, new Measures of Effectiveness (MOEs) are introduced from AI and ML including Precision, Recall, and F-score. The new and the original MOEs from Hatzinger and Gertsman are analyzed using Analysis of Variance (ANOVA) and covariance matrix. The results of this research show the algorithm does not have a significant effect against the original MOEs or the new MOEs which is likely due to a similar spreading of the Networked Collaborative Autonomous Munition (NCAM) as compared to Hatzinger and Gertsman. The results are negatively correlated to a decrease in target distributions standard deviation i.e. target clustering. This second result is more surprising as tighter target distributions could result in less area to search, but the NCAM continue to distribute their locations regardless of clusters identified

Optimizing combat capabilities by modeling combat as a complex adaptive system

Procuring combat systems in the Department of Defense is a balancing act where many variables, only some under control of the department, shift simultaneously. Technology changes non-linearly, providing new opportunities and new challenges to the existing and potential force. Money available changes year over year to fit into the overall US Government budget. Numbers of employees change through political demands rather than by cost-effectiveness considerations. The intent is to provide the best mix of equipment to field the best force against an expected enemy while maintaining adequate capability against the unexpected. Confounding this desire is the inability of current simulations to dynamically model changing capabilities and the very large universe of potential combinations of equipment and tactics.;The problem can be characterized as a stochastic, mixed-integer, non-linear optimization problem. This dissertation proposes to combine an agent-based model developed to test solutions that constitute both equipment capabilities and tactics with a co-evolutionary genetic algorithm to search this hyper-dimensional solution space. In the process, the dissertation develops the theoretical underpinning for using agent-based simulations to model combat. It also provides the theoretical basis for improvement of search effectiveness by co-evolving multiple systems simultaneously, which increases exploitation of good schemata and widens exploration of new schemata. Further, it demonstrates the effectiveness of using agent-based models and co-evolution in this application confirming the theoretical results.;An open research issue is the value of increased information in a system. This dissertation uses the combination of an agent-based model with a co-evolutionary genetic algorithm to explore the value added by increasing information in a system. The result was an increased number of fit solutions, rather than an increase in the fitness of the best solutions. Formerly unfit solutions were improved by increasing the information available making them competitive with the most fit solutions whereas already fit solutions were not improved

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Cyber-physical Systems (CPS) Security: State of the Art and Research Opportunities for Information Systems Academics

Attacks on cyber-physical systems (CPS) continue to grow in frequency. However, cybersecurity academics and practitioners have so far focused primarily on computer systems and networks rather than CPS. Given the alarming frequency with which cybercriminals attack CPS and the unique cyber-physical relationship in CPS, we propose that CPS security needs go beyond what purely computer and network security requires. Thus, we require more focused research on cybersecurity based on the cyber-physical relationship between various CPS components. In this paper, we stock of the current state of CPS security and identify research opportunities for information systems (IS) academics