Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model

Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS) in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.Defence Science Journal, 2013, 63(5), pp.497-501, DOI:http://dx.doi.org/10.14429/dsj.63.371

Evolving Effective Micro Behaviors for Real-Time Strategy Games

Real-Time Strategy games have become a new frontier of artificial intelligence research. Advances in real-time strategy game AI, like with chess and checkers before, will significantly advance the state of the art in AI research. This thesis aims to investigate using heuristic search algorithms to generate effective micro behaviors in combat scenarios for real-time strategy games. Macro and micro management are two key aspects of real-time strategy games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes against equal numbers of opponent units or win even when outnumbered. In this research, we use influence maps and potential fields as a basis representation to evolve micro behaviors. We first compare genetic algorithms against two types of hill climbers for generating competitive unit micro management. Second, we investigated the use of case-injected genetic algorithms to quickly and reliably generate high quality micro behaviors. Then we compactly encoded micro behaviors including influence maps, potential fields, and reactive control into fourteen parameters and used genetic algorithms to search for a complete micro bot, ECSLBot. We compare the performance of our ECSLBot with two state of the art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular real-time strategy game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. In addition, the same approach works to create competitive micro behaviors in another game SeaCraft. Using parallelized genetic algorithms to evolve parameters in SeaCraft we are able to speed up the evolutionary process from twenty one hours to nine minutes. We believe this work provides evidence that genetic algorithms and our representation may be a viable approach to creating effective micro behaviors for winning skirmishes in real-time strategy games

APSO based automated planning in Constructive Simulation

Constructive simulations are the applications used by the military for the training of their commanders in planning and analysis of various threats and Courses of Action. In the ‘analysis wargames’, there are need to automate many of the tasks of the commander which are carried out by subunit commanders on the ground. Deployment of defence units is one of such important decision making by commander. Deployments of units (and sub units) is dependent on multiple factors which needs to be satisfied/optimised for meeting the given objective of the unit. In this paper we have attempted to solve the multi criterion decision problem of optimal deployment of defence units in mountainous terrain using Particle Swarm Optimization(PSO) and Adaptive Particle Swarm Optimization(APSO). The algorithm has been tested with varied number of decision parameters and their weights using digital elevation and vector data of the terrain features. The auto deployment outcomes are found satisfactory. Our solution approach has potential in automated planning in constructive simulations.   &nbsp

A Data Fusion System for Simulation of Critical Scenarios and Decision-Making

The decision-making (DM) process in critical environments is a complex process that can be simulated due to current telematic capabilities, which allow the real time interaction of large amounts of data. This document describes the proposed architecture from a research process, developed by the FAC Aerospace Technology Development Center (CETAD), where using computational and expert system tools, allowed to create a computational environment for decision maker evaluated his options to prepares for real events, simulating characteristics, resources and strategies in a real time environment. This document describes an investigation product resulted in a simulation system, based on a combination of fuzzy logic, genetic algorithms and decision trees which let modelled and simulated various entities and their automatic response according to simulated patterns and situations, in which, through operators, decision maker can modify entities behaviour, according to parameterized restrictions and physical conditions. Also based on business intelligence tools, reports are generated to evaluate the decisions made. This type of technologies improves planning capacity and facilitate the decision-making process. System allows simulating any media deployment in national security and critical events context. Thus, a case study was developed for implementation of a support in natural disaster scenario simulatio

A technique for determining viable military logistics support alternatives

A look at today's US military will see them operating much beyond the scope of protecting and defending the United States. These operations now consist of, but are not limited to humanitarian aid, disaster relief, and conflict resolution. This broad spectrum of operational environments has necessitated a transformation of the individual military services into a hybrid force that can leverage the inherent and emerging capabilities from the strengths of those under the umbrella of the Department of Defense (DOD), this concept has been coined Joint Operations. Supporting Joint Operations requires a new approach to determining a viable military logistics support system. The logistics architecture for these operations has to accommodate scale, time, varied mission objectives, and imperfect information. Compounding the problem is the human in the loop (HITL) decision maker (DM) who is a necessary component for quickly assessing and planning logistics support activities. Past outcomes are not necessarily good indicators of future results, but they can provide a reasonable starting point for planning and prediction of specific needs for future requirements. Adequately forecasting the necessary logistical support structure and commodities needed for any resource intensive environment has progressed well beyond stable demand assumptions to one in which dynamic and nonlinear environments can be captured with some degree of fidelity and accuracy. While these advances are important, a holistic approach that allows exploration of the operational environment or design space does not exist to guide the military logistician in a methodical way to support military forecasting activities. To bridge this capability gap, a method called A Technique for Logistics Architecture Selection (ATLAS) has been developed. This thesis describes and applies the ATLAS method to a notional military scenario that involves the Navy concept of Seabasing and the Marine Corps concept of Distributed Operations applied to a platoon sized element. This work uses modeling and simulation to incorporate expert opinion and knowledge of military operations, dynamic reasoning methods, and certainty analysis to create a decisions support system (DSS) that can be used to provide the DM an enhanced view of the logistics environment and variables that impact specific measures of effectiveness.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Fahringer, Philip; Committee Member: Nixon, Janel; Committee Member: Schrage, Daniel; Committee Member: Soban, Danielle; Committee Member: Vachtsevanos, Georg

Methods of multi-agent movement control and coordination of groups of mobile units in a real-time strategy games

Tato práce nabízí metodu pro reaktivní řízení jednotek v real-time strategické (RTS) počitačové hře pomocí multi-agentních potenciálových polí. Klasická RTS hra StarCraft: Broodwar byla vybrána jako testovací platforma díky jejímu postavení na konkurenční scéně umělé inteligence (UI). Nabízená umělá inteligence ovládá své jednotky pomocí umístění různých potenciálových polí na objekty a na místa v herním světě. Snahou této práce je vylepšit předchozí metody využívajicí potenciálová pole.This thesis proposes an approach to Reactive Control in Real-Time Strategy (RTS) computer games using Multi-Agent Potential Fields. The classic RTS title StarCraft: Brooodwar has been chosen as testing platform due to its status in the competitive Artificial Intelligence (AI) scene. The proposed AI controls its units by placing different types of potential fields in objects and places around the game world. This work is an attempt to improve previous methods done with Potential Field in RTS

Air Force Institute of Technology Research Report 1997

This report summarizes the research activities of the Air Force Institute of Technology\u27s Graduate School of Engineering and the Graduate School of Logistics and Acquisition Management. It describes research interests and faculty expertise; list student theses/dissertations; identifies research sponsors and contributions; and outlines the procedure for contacting either school

Air Force Institute of Technology Research Report 2001

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment

A Survey on Weapon Target Allocation Models and Applications

In Command and Control (C2), Threat Evaluation (TE) and Weapon Target Allocation (WTA) are two key components. To build an automated system in this area after modeling Threat Evaluation and Weapon Target Allocation processes, solving these models and finding the optimal solution are further important issues. This setting demands instantaneous operational planning and decision making under inherent severe stress conditions. The associated responsibilities are usually divided among a number of operators and also computerized decision support systems that aid these operators during the decision making process. In this Chapter, the literature in the area of WTA system with the emphasis on the modeling and solving methods are surveyed