Artificial Intelligence and Robotics Commons^™

Explainable Neural Networks Based Anomaly Detection For Cyber-Physical Systems, Kasun Amarasinghe

Theses and Dissertations

Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential.

The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical Systems (CP-ADS). The main objective was broken down into three sub-objectives: 1) Identifying key-requirements that an …

Curtus: An Nlp Tool To Map Job Skills To Academic Courses, Daniel Rockwell

Theses and Dissertations

Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses …

Distributed Multi-Label Learning On Apache Spark, Jorge Gonzalez Lopez

Theses and Dissertations

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes …

Al Planning Assistant For Scheduling Daily Activities, Priyanka Ahuja

Theses and Dissertations

Artificial conversational agents are software agents that can interact with humans in the way humans do. Siri Cortana, and Alexa are examples of intelligent agents that can help us with almost all the basic tasks. These agents are smart enough to do the basic tasks, but not as much when it comes to complex tasks, such as analyzing traffic data, reviewing scheduling conflicts, rescheduling meetings while resolving conflicts, and offering suggestions based upon data analyses (e.g. traffic patterns, weather, etc.) The actual potential of dialogue-based task agent potential remains untapped. The reason is the fact agents lack the ability to …

Behavior Flexibility For Autonomous Unmanned Aerial Systems, Taylor B. Bodin

Theses and Dissertations

Autonomous unmanned aerial systems (UAS) could supplement and eventually subsume a substantial portion of the mission set currently executed by remote pilots, making UAS more robust, responsive, and numerous than permitted by teleoperation alone. Unfortunately, the development of robust autonomous systems is difficult, costly, and time-consuming. Furthermore, the resulting systems often make little reuse of proven software components and offer limited adaptability for new tasks. This work presents a development platform for UAS which promotes behavioral flexibility. The platform incorporates the Unified Behavior Framework (a modular, extensible autonomy framework), the Robotic Operating System (a RSF), and PX4 (an open- source …

Target Detection Using Convolutional Neural Networks, Robert P. Loibl

Theses and Dissertations

This research explores the use of Convolutional Neural Networks (CNNs) to classify targets of interest within satellite imagery. Methods were specifically devised for the classification of airports within Landsat-8 scenes. A novel automated dataset generation technique was developed to create labeled datasets from satellite imagery using only coordinate metadata. Using this approach a very large dataset of over 132,000 labeled images was created without human input. This dataset was used to evaluate the effects of color and resolution on airport classification accuracy. Two experiments were run with the first experiment classifying large airports with 96.8% accuracy, and the second classifying …

Novel Support Vector Machines For Diverse Learning Paradigms, Gabriella A. Melki

Theses and Dissertations

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification.

Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets' correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model's prediction performance, while reducing computational complexity.

Under the multi-instance paradigm, …

Big Networks: Analysis And Optimal Control, Hung The Nguyen

Theses and Dissertations

The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world …

A Sandbox In Which To Learn And Develop Soar Agents, Daniel Lugo

Theses and Dissertations

It is common for military personnel to leverage simulations (and simulators) as cost-effective tools to train and become proficient at various tasks (e.g., flying an aircraft and/or performing a mission, among others). These training simulations often need to represent humans within the simulated world in a realistic manner. Realistic implies creating simulated humans that exhibit behaviors that mimic real-world decision making and actions. Typically, to create the decision-making logic, techniques developed from the domain of artificial intelligence are used. Although there are several approaches to developing intelligent agents; we focus on leveraging and open source project called Soar, to define …

Exploring Algorithms To Recognize Similar Board States In Arimaa, Malik Khaleeque Ahmed

Theses and Dissertations

The game of Arimaa was invented as a challenge to the field of game-playing artificial intelligence, which had grown somewhat haughty after IBM's supercomputer Deep Blue trounced world champion Kasparov at chess. Although Arimaa is simple enough for a child to learn and can be played with an ordinary chess set, existing game-playing algorithms and techniques have had a difficult time rising up to the challenge of defeating the world's best human Arimaa players, mainly due to the game's impressive branching factor. This thesis introduces and analyzes new algorithms and techniques that attempt to recognize similar board states based on …

Rationality, Parapsychology, And Artificial Intelligence In Military And Intelligence Research By The United States Government In The Cold War, Guy M. Lomeo

Theses and Dissertations

A study analyzing the roles of rationality, parapsychology, and artificial intelligence in military and intelligence research by the United States Government in the Cold War. An examination of the methodology behind the decisions to pursue research in two fields that were initially considered irrational.

Automated Conjecturing Approach For Benzenoids, David Muncy

Theses and Dissertations

Benzenoids are graphs representing the carbon structure of molecules, defined by a closed path in the hexagonal lattice. These compounds are of interest to chemists studying existing and potential carbon structures. The goal of this study is to conjecture and prove relations between graph theoretic properties among benzenoids. First, we generate conjectures on upper bounds for the domination number in benzenoids using invariant-defined functions. This work is an extension of the ideas to be presented in a forthcoming paper. Next, we generate conjectures using property-defined functions. As the title indicates, the conjectures we prove are not thought of on our …

Eeg Interictal Spike Detection Using Artificial Neural Networks, Howard J. Carey Iii

Theses and Dissertations

Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce …

Adaptive Automation Design And Implementation, Jason M. Bindewald

Theses and Dissertations

Automations allow us to reduce the need for humans in certain environments, such as auto-pilot features on unmanned aerial vehicles. However, some situations still require human intervention. Adaptive automation is a research field that enables computer systems to adjust the amount of automation by taking over tasks from or giving tasks back to the user. This research develops processes and insights for adaptive automation designers to take theoretical adaptive automation ideas and develop them into real-world adaptive automation system. These allow developers to design better automation systems that recognize the limits of computers systems, enabling better designs for systems in …

The Unified Behavior Framework For The Simulation Of Autonomous Agents, Daniel M. Roberson

Theses and Dissertations

Since the 1980s, researchers have designed a variety of robot control architectures intending to imbue robots with some degree of autonomy. A recently developed architecture, the UBF, implements a variation of the three-layer architecture with a reactive controller to rapidly make behavior decisions. Additionally, the UBF utilizes software design patterns that promote the reuse of code and free designers to dynamically switch between behavior paradigms. This paper explores the application of the UBF to the simulation domain. By employing software engineering principles to implement the UBF architecture within an open-source simulation framework, we have extended the versatility of both. The …

Tactical Ai In Real Time Strategy Games, Donald A. Gruber

Theses and Dissertations

The real time strategy (RTS) tactical decision making problem is a difficult problem. It is generally more complex due to its high degree of time sensitivity. This research effort presents a novel approach to this problem within an educational, teaching objective. Particular decision focus is target selection for a artificial intelligence (AI) RTS game model. The use of multi-objective evolutionary algorithms (MOEAs) in this tactical decision making problem allows an AI agent to make fast, effective solutions that do not require modification to the current environment. This approach allows for the creation of a generic solution building tool that is …

Unified Behavior Framework For Discrete Event Simulation Systems, Alexander J. Kamrud

Theses and Dissertations

Intelligent agents provide simulations a means to add lifelike behavior in place of manned entities. Generally when developed, a single intelligent agent model is chosen, such as rule based, behavior trees, etc. This choice introduces restrictions into what behaviors agents can manifest, and can require significant testing in edge cases. This thesis presents the use of the UBF in the AFSIM environment. The UBF provides the flexibility to implement any and all intelligent agent models, allowing the developer to choose the model he/she feels best fits the experiment at hand. Furthermore, the UBF demonstrates several key software engineering principles through …

Graph-Based Regularization In Machine Learning: Discovering Driver Modules In Biological Networks, Xi Gao

Theses and Dissertations

Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel's law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today's scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, …

Holistic Network Defense: Fusing Host And Network Features For Attack Classification, Jenny W. Ji

Theses and Dissertations

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon …

Kernelized Locality-Sensitive Hashing For Fast Image Landmark Association, Mark A. Weems

Theses and Dissertations

As the concept of war has evolved, navigation in urban environments where GPS may be degraded is increasingly becoming more important. Two existing solutions are vision-aided navigation and vision-based Simultaneous Localization and Mapping (SLAM). The problem, however, is that vision-based navigation techniques can require excessive amounts of memory and increased computational complexity resulting in a decrease in speed. This research focuses on techniques to improve such issues by speeding up and optimizing the data association process in vision-based SLAM. Specifically, this work studies the current methods that algorithms use to associate a current robot pose to that of one previously …

A Study Of Three Artificial Neural Networks Models' Ability To Identify Emotions From Facial Images, Timothy Scott Hyde

Theses and Dissertations

Facial expressions conveying emotions are vital for human communication. They are also important in the studies of human interaction and behavioral studies. Recognition of emotions, using facial images, may provide a fast and practical approach that is noninvasive. Most previous studies of emotion recognition through facial images were based on the Facial Action Coding System (FACS). The FACS, which was developed by Ekman and Freisen in 1978, was created to identify different facial muscular actions. Previous artificial neural network-based approaches for classification of facial expressions focused on improving one particular neural network model for better accuracy. The purpose of this …

Developing An Effective And Efficient Real Time Strategy Agent For Use As A Computer Generated Force, Kurt Weissgerber

Theses and Dissertations

Computer Generated Forces (CGF) are used to represent units or individuals in military training and constructive simulation. The use of CGF significantly reduces the time and money required for effective training. For CGF to be effective, they must behave as a human would in the same environment. Real Time Strategy (RTS) games place players in control of a large force whose goal is to defeat the opponent. The military setting of RTS games makes them an excellent platform for the development and testing of CGF. While there has been significant research in RTS agent development, most of the developed agents …

Coalition Formation Under Uncertainty, Daylond J. Hooper

Theses and Dissertations

Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents …

Evolutionary Artificial Neural Network Weight Tuning To Optimize Decision Making For An Abstract Game, Corey M. Miller

Theses and Dissertations

Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors’ performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a …

Behavior-Based Power Management In Autonomous Mobile Robots, Charles A. Fetzek

Theses and Dissertations

Current attempts to prolong the life of a robot on a single battery charge focus on lowering the operating frequency of the onboard hardware, or allowing devices to go to sleep during idle states. These techniques have much overhead and do not come built in to the underlying robotic architecture. In this thesis, battery life is greatly extended through development of a behavior-based power management system, including a Markov decision process power planner, thereby allowing future robots increased time to operate and loiter in their required domain. Behavior-based power management examines sensors needed by the currently active behavior set and …

Dynamic Behavior Sequencing In A Hybrid Robot Architecture, Jeffrey P. Duffy

Theses and Dissertations

Hybrid robot control architectures separate plans, coordination, and actions into separate processing layers to provide deliberative and reactive functionality. This approach promotes more complex systems that perform well in goal-oriented and dynamic environments. In various architectures, the connections and contents of the functional layers are tightly coupled so system updates and changes require major changes throughout the system. This work proposes an abstract behavior representation, a dynamic behavior hierarchy generation algorithm, and an architecture design to reduce this major change incorporation process. The behavior representation provides an abstract interface for loose coupling of behavior planning and execution components. The hierarchy …

Exploitation Of Self Organization In Uav Swarms For Optimization In Combat Environments, Dustin J. Nowak

Theses and Dissertations

This investigation focuses primarily on the development of effective target engagement for unmanned aerial vehicle (UAV) swarms using autonomous self-organized cooperative control. This development required the design of a new abstract UAV swarm control model which flows from an abstract Markov structure, a Partially Observable Markov Decision Process. Self-organization features, bio-inspired attack concepts, evolutionary computation (multi-objective genetic algorithms, differential evolution), and feedback from environmental awareness are instantiated within this model. The associated decomposition technique focuses on the iterative deconstruction of the problem domain state and dynamically building-up of self organizational rules as related to the problem domain environment. Resulting emergent …

Conceptual Study Of Rotary-Wing Microrobotics, Kelson D. Chabak

Theses and Dissertations

This thesis presents a novel rotary-wing micro-electro-mechanical systems (MEMS) robot design. Two MEMS wing designs were designed, fabricated and tested including one that possesses features conducive to insect level aerodynamics. Two methods for fabricating an angled wing were also attempted with photoresist and CrystalBond™ to create an angle of attack. One particular design consisted of the wing designs mounted on a gear which are driven by MEMS actuators. MEMS comb drive actuators were analyzed, simulated and tested as a feasible drive system. The comb drive resonators were also designed orthogonally which successfully rotated a gear without wings. With wings attached …

A Secure Group Communication Architecture For A Swarm Of Autonomous Unmanned Aerial Vehicles, Adrian N. Phillips

Theses and Dissertations

This thesis investigates the application of a secure group communication architecture to a swarm of autonomous unmanned aerial vehicles (UAVs). A multicast secure group communication architecture for the low earth orbit (LEO) satellite environment is evaluated to determine if it can be effectively adapted to a swarm of UAVs and provide secure, scalable, and efficient communications. The performance of the proposed security architecture is evaluated with two other commonly used architectures using a discrete event computer simulation developed using MatLab. Performance is evaluated in terms of the scalability and efficiency of the group key distribution and management scheme when the …

Robot Controller Architecture: Layered Mode Selection Logic With Fuzzy Sensor Fusion Network, Traig E.B. Born

Theses and Dissertations

A behavior based robot controller was implemented using Layered Mode Selection Logic as a behavior coordination mechanism and a Fuzzy Sensor Fusion Network to provide perception. A series of collision experiments were conducted to create a Fuzzy Sensor Fusion Network capable of detecting collisions using acceleration readings from wheel encoders. An obstacle avoidance behavior was created, and it was demonstrated that the robot could be constrained by movable obstacles. The avoidance behavior was modified to create an obstacle manipulation behavior. This behavior allowed the robot to escape when enclosed by movable obstacles. The fuzzy sensor fusion network was enhanced to …