Artificial Intelligence and Robotics Commons^™

Ai Education: Birds Of A Feather, Todd W. Neller

Computer Science Faculty Publications

Games are beautifully crafted microworlds that invite players to explore complex terrains that spring into existence from even simple rules. As AI educators, games can offer fun ways of teaching important concepts and techniques. Just as Martin Gardner employed games and puzzles to engage both amateurs and professionals in the pursuit of Mathematics, a well-chosen game or puzzle can provide a catalyst for AI learning and research. [excerpt]

Modeling, Learning And Reasoning About Preference Trees Over Combinatorial Domains, Xudong Liu

Theses and Dissertations--Computer Science

In my Ph.D. dissertation, I have studied problems arising in various aspects of preferences: preference modeling, preference learning, and preference reasoning, when preferences concern outcomes ranging over combinatorial domains. Preferences is a major research component in artificial intelligence (AI) and decision theory, and is closely related to the social choice theory considered by economists and political scientists. In my dissertation, I have exploited emerging connections between preferences in AI and social choice theory. Most of my research is on qualitative preference representations that extend and combine existing formalisms such as conditional preference nets, lexicographic preference trees, answer-set optimization programs, possibilistic …

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 …

Enabling Carrier Collaboration Via Order Sharing Double Auction: A Singapore Urban Logistics Perspective, Handoko Stephanus Daniel, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

A recent exploratory study on the collaborative urban logistics in Singapore suggests that cost reduction and privacy preservation are two main drivers that would motivate the participation of carriers in consolidating their last mile deliveries. With Singapore's mild restrictions on the vehicle types or the time windows for the last-mile delivery, we believe that with proper technology in place, an Urban Consolidation Center like the Tenjin Joint Distribution System in Fukuoka Japan may be implemented to achieve cost reduction with some degree of privacy preservation. Participating carriers keep their respective private orders and have the option to get their remaining …

Using Genetic Algorithms To Evolve Artificial Neural Networks, William T. Kearney

Honors Theses

This paper demonstrates that neuroevolution is an effective method to determine an optimal neural network topology. I provide an overview of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, and describe how unique characteristics of this algorithm solve various problem inherent to neuroevolution (namely the competing conventions problem and the challenges associated with protecting topological innovation). Parallelization is shown to greatly speed up efficiency, further reinforcing neuroevolution as a potential alternative to traditional backpropagation. I also demonstrate that appropriate parameter selection is critical in order to efficiently converge to an optimal topology. Lastly, I produce an example solution to a medical …

Evaluation Of Supervised Machine Learning For Classifying Video Traffic, Farrell R. Taylor

CCAC Theses and Dissertations

Operational deployment of machine learning based classifiers in real-world networks has become an important area of research to support automated real-time quality of service decisions by Internet service providers (ISPs) and more generally, network administrators. As the Internet has evolved, multimedia applications, such as voice over Internet protocol (VoIP), gaming, and video streaming, have become commonplace. These traffic types are sensitive to network perturbations, e.g. jitter and delay. Automated quality of service (QoS) capabilities offer a degree of relief by prioritizing network traffic without human intervention; however, they rely on the integration of real-time traffic classification to identify applications. Accordingly, …

Automatically Defined Templates For Improved Prediction Of Non-Stationary, Nonlinear Time Series In Genetic Programming, David Moskowitz

CCAC Theses and Dissertations

Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions.

Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to …

Persuasion In Online Communication : Automation And Counteraction, Samira Shaikh Shaikh

Legacy Theses & Dissertations (2009 - 2024)

In this thesis, we studied persuasion in online communication and how to automate

Cp-Nets: From Theory To Practice, Thomas E. Allen

Theses and Dissertations--Computer Science

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must …

Preferences: Optimization, Importance Learning And Strategic Behaviors, Ying Zhu

Theses and Dissertations--Computer Science

Preferences are fundamental to decision making and play an important role in artificial intelligence. Our research focuses on three group of problems based on the preference formalism Answer Set Optimization (ASO): preference aggregation problems such as computing optimal (near optimal) solutions, strategic behaviors in preference representation, and learning ranks (weights) for preferences.

In the first group of problems, of interest are optimal outcomes, that is, outcomes that are optimal with respect to the preorder defined by the preference rules. In this work, we consider computational problems concerning optimal outcomes. We propose, implement and study methods to compute an optimal outcome; …

Implementation Of An Air Supply Unit Control Scheme For The Uc2av (Unmanned Circulation Control Aerial Vehicle), Cameron Rosen

Electronic Theses and Dissertations

The expanded prevalence of Unmanned Aerial Vehicles (UAVs) in recent years has created many opportunities to research novel applications for their use, enabled by the reduced cost, mission flexibility, and reduced risk that small-scale unmanned platforms provide in comparison to larger aircraft. Despite the versatility of unmanned aviation, limitations on payload size and weight, fuel and power capacity, and takeoff and landing infrastructure can restrict UAV applications, and have created a need for lift augmenting technologies that can reduce the impact of these limitations. Circulation Control (CC) is an active flow technique that has been proven as a method for …

Automated Design Of Boolean Satisfiability Solvers Employing Evolutionary Computation, Alex Raymond Bertels

Masters Theses

"Modern society gives rise to complex problems which sometimes lend themselves to being transformed into Boolean satisfiability (SAT) decision problems; this thesis presents an example from the program understanding domain. Current conflict-driven clause learning (CDCL) SAT solvers employ all-purpose heuristics for making decisions when finding truth assignments for arbitrary logical expressions called SAT instances. The instances derived from a particular problem class exhibit a unique underlying structure which impacts a solver's effectiveness. Thus, tailoring the solver heuristics to a particular problem class can significantly enhance the solver's performance; however, manual specialization is very labor intensive. Automated development may apply hyper-heuristics …

Using Topic Modelling Algorithms For Hierarchical Activity Discovery, Eoin Rogers, John D. Kelleher, Robert J. Ross

Conference papers

Activity discovery is the unsupervised process of discovering patterns in data produced from sensor networks that are monitoring the behaviour of human subjects. Improvements in activity discovery may simplify the training of activity recognition models by enabling the automated annotation of datasets and also the construction of systems that can detect and highlight deviations from normal behaviour. With this in mind, we propose an approach to activity discovery based on topic modelling techniques, and evaluate it on a dataset that mimics complex, interleaved sensor data in the real world. We also propose a means for discovering hierarchies of aggregated activities …

Harnessing The Power Of Text Mining For The Detection Of Abusive Content In Social Media, Hao Chen, Susan Mckeever, Sarah Jane Delany

Conference papers

Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and …

Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus

Dissertations, Master's Theses and Master's Reports

The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which …

Mining Human Activity Using Dimensionality Reduction And Pattern Recognition, Ismail El Moudden, Mounir Ouzir, Badreddine Benyacoub, Souad El Bernoussi

Research and Infrastructure Service Enterprise (RISE) Faculty Publications

Human activity recognition (HAR) is an emerging research topic in pattern recognition, especially in computer vision. The main objective of human activity recognition is to automatically detect and analyze human activities from the information acquired from different sensors. Human activity prediction using big data remains a challengingly open problem. Several approaches have recently been developed in order to find practical ways to solve high dimensionality of data problems. The aim of this study is to attempt, using data mining techniques, to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and …

Integrating Cobots In Engineering Technology Education, Ana M. Djuric, Vukica Jovanovic, Tatiana V. Goris, Otilia Popescu

Engineering Technology Faculty Publications

Collaborative robots or CoBots, unlike traditional robots, are safe and flexible enough to work harmoniously with humans. Exploiting the efficiency of automated operations and the flexibility of manual operations in one process can improve productivity and worker job satisfaction. CoBots technology has been experiencing strong growth in different areas such as ground transportation, food-processing industry, car manufacturing, and naval or aeronautical engineering. Current CoBots education and training opportunities are rare or non-existent in university environments. In response to this need, we developed several CoBots modules which will be integrated in the current robotics and mechatronics courses. In this paper we …

Improving The Vector Auto Regression Technique For Time-Series Link Prediction By Using Support Vector Machine, Proceso L. Fernandez Jr, Jan Miles Co

Department of Information Systems & Computer Science Faculty Publications

Predicting links between the nodes of a graph has become an important Data Mining task because of its direct applications to biology, social networking, communication surveillance, and other domains. Recent literature in time-series link prediction has shown that the Vector Auto Regression (VAR) technique is one of the most accurate for this problem. In this study, we apply Support Vector Machine (SVM) to improve the VAR technique that uses an unweighted adjacency matrix along with 5 matrices: Common Neighbor (CN), Adamic-Adar (AA), Jaccard’s Coefficient (JC), Preferential Attachment (PA), and Research Allocation Index (RA). A DBLP dataset covering the years from …

Applications Of Computational Geometry And Computer Vision, Joseph Lemley

All Master's Theses

Recent advances in machine learning research promise to bring us closer to the original goals of artificial intelligence. Spurred by recent innovations in low-cost, specialized hardware and incremental refinements in machine learning algorithms, machine learning is revolutionizing entire industries. Perhaps the biggest beneficiary of this progress has been the field of computer vision. Within the domains of computational geometry and computer vision are two problems: Finding large, interesting holes in high dimensional data, and locating and automatically classifying facial features from images. State of the art methods for facial feature classification are compared and new methods for finding empty hyper-rectangles …

The Reconfigurable Machinery Efficient Workspace Analysis Based On The Twist Angles, Ana M. Djuric, Vukica Jovanovic, Mirjana Filipovic, Ljubinko Kevac

Engineering Technology Faculty Publications

A novel methodology for the calculation, visualisation and analysis of the Reconfigurable Machinery Efficient Workspace (RMEW), based on the twist angles, is presented in this paper. The machinery's kinematic parameters are used for calculating the workspace, while the efficient workspace is associated with the machinery's path and includes the end-effector position and orientation. To analyse and visualise many different machinery efficient workspaces at the same time, the calculation is based on the previously developed and validated complex reconfigurable machinery's kinematic structure named n-DOF Global Kinematic Model (n-GKM). An industrial robot is used as an example to demonstrate …