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Smt-Based Constraint Answer Set Solver Ezsmt (System Description), Benjamin Susman, Yuliya Lierler 2016 University of Nebraska at Omaha

Smt-Based Constraint Answer Set Solver Ezsmt (System Description), Benjamin Susman, Yuliya Lierler

Yuliya Lierler

Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. Recently, the formal link between this research area and satisfiability modulo theories (or SMT) was established. This link allows the cross-fertilization between traditionally different solving technologies. The paper presents the system EZSMT, one of the first SMT-based solvers for constraint answer set programming. It also presents the comparative analysis of the performance of EZSMT in relation to its peers including solvers EZCSP and CLINGCON that rely on the hybrid solving approach based on the combination of answer set solvers and constraint solvers. Experimental ...


Constraint Cnf: A Sat And Csp Language Under One Roof, Broes De Cat, Yuliya Lierler 2016 KU Leuven

Constraint Cnf: A Sat And Csp Language Under One Roof, Broes De Cat, Yuliya Lierler

Yuliya Lierler

A new language, called constraint CNF, is proposed. It integrates propositional logic with constraints stemming from constraint programming (CP). A family of algorithms is designed to solve problems expressed in constraint CNF. These algorithms build on techniques from both propositional satisfiability (SAT) and CP. The result is a uniform language and an algorithmic framework, which allow us to gain a deeper understanding of the relation between the solving techniques used in SAT and in CP and apply them together.


A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury 2016 The Graduate Center, City University of New York

A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS).

This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one ...


Communication, Machines & Human Augmentics, John Novak, Jason Archer, Victor Mateevitsi, Steve Jones 2016 The University of Illinois at Chicago

Communication, Machines & Human Augmentics, John Novak, Jason Archer, Victor Mateevitsi, Steve Jones

communication +1

This essay reformulates the question of human augmentation as a problem of advanced human-machine communication, theorizing that such communication implies robust artificial intelligence and necessitates understanding the relational role new technologies play in human-machine communication. We focus on the questions, “When do electronic tools cease to be ‘simply’ tools, and become meaningfully part of ourselves,” and, “When might we think of these tools as augmenting our selves, rather than simply amplifying our capabilities?” These questions, already important to the medical and rehabilitative fields, loom larger with increasing commodification of pervasive augmentation technologies, and indicate the verge on which human-machine communication ...


Pagi World: A Physically Realistic, General-Purpose Simulation Environment For Developmental Ai Systems, John Licato, Selmer Bringsjord 2016 Indiana University - Purdue University Fort Wayne

Pagi World: A Physically Realistic, General-Purpose Simulation Environment For Developmental Ai Systems, John Licato, Selmer Bringsjord

Computer Science Faculty Presentations

There has long been a need for a simulation environment rich enough to support the development of an AI system sufficiently knowledgeable about physical causality to pass certain tests of Psychometric Artificial Intelligence (PAI) and Psychometric Artificial General Intelligence (PAGI). In this article, we present a simulation environment, PAGI World, which is: cross-platform (as it can be run on all major operating systems); open-source (and thus completely free of charge to use); able to work with AI systems written in almost any programming language; as agnostic as possible regarding which AI approach is used; and easy to set up and ...


Agora: A Knowledge Marketplace For Machine Learning, Mauro Ribeiro 2016 The University of Western Ontario

Agora: A Knowledge Marketplace For Machine Learning, Mauro Ribeiro

Electronic Thesis and Dissertation Repository

More and more data are becoming part of people's lives. With the popularization of technologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models ...


Toward Autonomous Multi-Rotor Indoor Aerial Vehicles, Connor Brooks 2016 Western Kentucky University

Toward Autonomous Multi-Rotor Indoor Aerial Vehicles, Connor Brooks

Honors College Capstone Experience/Thesis Projects

In this project, we worked to create an indoor autonomous micro aerial vehicle (MAV) using a multi-layer architecture with modular hardware and software components. We required that all computing was done onboard the vehicle during time of flight so that no remote connection of any kind was necessary for successful control of the vehicle, even when flying autonomously. We utilized environmental sensors including ultrasonic sensors, light detection and ranging modules, and inertial measurement units to acquire necessary environment information for autonomous flight. We used a three-layered system that combined a modular control architecture with distributed on-board computing to allow for ...


V3nlp Framework: Tools To Build Applications For Extracting Concepts From Clinical Text, Guy Divita, Marjorie Carter MS, Le-Thuy Tran PhD, Doug Redd MS, Qing T. Zeng PhD, Scott Duvall PhD, Matthew H. Samore MD, PhD, Adi V. Gundlapalli MD, PhD, MS 2016 VA Salt Lake City Health Care System and University of Utah School of Medicine

V3nlp Framework: Tools To Build Applications For Extracting Concepts From Clinical Text, Guy Divita, Marjorie Carter Ms, Le-Thuy Tran Phd, Doug Redd Ms, Qing T. Zeng Phd, Scott Duvall Phd, Matthew H. Samore Md, Phd, Adi V. Gundlapalli Md, Phd, Ms

eGEMs (Generating Evidence & Methods to improve patient outcomes)

Introduction: Substantial amounts of clinically significant information are contained only within the narrative of the clinical notes in electronic medical records. v3NLP Framework is a set of best of breed functionalities developed to transform this information into structured data for use in quality improvement, research, population health surveillance, and decision support.

Background: MetaMap, cTAKES and similar well known NLP tools do not have sufficient scalability out of the box. v3NLP Framework evolved out of the necessity to scale these tools up and provide a framework to customize and tune techniques to fit a variety of tasks, including document classification, tuned ...


Paper-Android-Based Health Care Management System, Fazal Masud Kundi, Anam Habib, ammara habib, Dr. Muhammad Zubair Asghar 2016 Gomal University, Dera Ismail Khan

Paper-Android-Based Health Care Management System, Fazal Masud Kundi, Anam Habib, Ammara Habib, Dr. Muhammad Zubair Asghar

Dr. Muhammad Zubair Asghar

The primary goal of this study is to develop an android-based healthcare application, which can assist the
users to monitor their health-related conditions for improving their health. Methods: The application is developed using android operating system environment. A Visual block programming language, namely MIT App Inventor is used to develop the system. The modification is presented as: (1) integration of different modules and their ffline usage, (2) history facility, (3) user friendly. The qualitative method is used to study the objective. Findings: The research paper depicts a brief study of existing systems and the new development that has made in ...


Classifying Pattern Formation In Materials Via Machine Learning, Lukasz Burzawa, Shuo Liu, Erica W. Carlson 2016 Purdue University

Classifying Pattern Formation In Materials Via Machine Learning, Lukasz Burzawa, Shuo Liu, Erica W. Carlson

The Summer Undergraduate Research Fellowship (SURF) Symposium

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated materials often reveal complex pattern formation that occurs on multiple length scales. We have shown in two disparate correlated materials that the pattern formation is driven by proximity to a disorder-driven critical point. We developed new analysis concepts and techniques that relate the observed pattern formation to critical exponents by analyzing the geometry and statistics of clusters observed in these experiments and converting that information into critical exponents. Machine learning algorithms can be helpful correlating data from scanning probe experiments to theoretical models ...


Conditional Computation In Deep And Recurrent Neural Networks, Andrew Scott Davis 2016 University of Tennessee, Knoxville

Conditional Computation In Deep And Recurrent Neural Networks, Andrew Scott Davis

Doctoral Dissertations

Recently, deep learning models such as convolutional and recurrent neural networks have displaced state-of-the-art techniques in a variety of application domains. While the computationally heavy process of training is usually conducted on powerful graphics processing units (GPUs) distributed in large computing clusters, the resulting models can still be somewhat heavy, making deployment in resource- constrained environments potentially problematic. In this work, we build upon the idea of conditional computation, where the model is given the capability to learn how to avoid computing parts of the graph. This allows for models where the number of parameters (and in a sense, the ...


A Genetic Algorithmic Approach To Automated Auction Mechanism Design, Jinzhong Niu, Simon Parsons 2016 CUNY Guttman Community College

A Genetic Algorithmic Approach To Automated Auction Mechanism Design, Jinzhong Niu, Simon Parsons

Publications and Research

In this paper, we present a genetic algorithmic approach to automated auction mechanism design in the context of \cat games. This is a follow-up to one piece of our prior work in the domain, the reinforcement learning-based grey-box approach. Our experiments show that given the same search space the grey-box approach is able to produce better auction mechanisms than the genetic algorithmic approach. The comparison can also shed light on the design and evaluation of similar search solutions to other domain problems.


Formalization Of The Ad Hominem Argumentation Scheme, Douglas Walton 2016 University of Windsor

Formalization Of The Ad Hominem Argumentation Scheme, Douglas Walton

Douglas Walton

In this paper, several examples from the literature, and one central new one, are used as case studies of texts of discourse containing an argumentation scheme that has now been widely investigated in literature on argumentation. Argumentation schemes represent common patterns of reasoning used in everyday conversational discourse. The most typical ones represent defeasible arguments based on nonmonotonic reasoning. Each scheme has a matching set of critical questions used to evaluate a particular argument fitting that scheme. The project is to study how to build a formal computational model of this scheme for the circumstantial ad hominem argument using argumentation ...


Examination Dialogue: An Argumentation Framework For Critically Questioning An Expert Opinion, Douglas Walton 2016 University of Windsor

Examination Dialogue: An Argumentation Framework For Critically Questioning An Expert Opinion, Douglas Walton

Douglas Walton

Recent work in argumentation theory (Walton and Krabbe, 1995; Walton, 2005) and artificial intelligence (Bench-Capon, 1992, 2003; Cawsey, 1992; McBurney and Parsons, 2002; Bench-Capon and Prakken, 2005) uses types of dialogue as contexts of argument use. This paper provides an analysis of a special type called examination dialogue, in which one party questions another party, sometimes critically or even antagonistically, to try to find out what that party knows about something. This type of dialogue is most prominent in law and in both legal and non-legal arguments based on expert opinion. It is also central to dialogue systems for questioning ...


Critical Questions In Computational Models Of Legal Argument, Douglas Walton, Thomas F. Gordon 2016 University of Windsor

Critical Questions In Computational Models Of Legal Argument, Douglas Walton, Thomas F. Gordon

Douglas Walton

Two recent computational models of legal argumentation, by Verheij and Gordon respectively, have interpreted critical questions as premises of arguments that can be defeated using Pollock’s concepts of undercutters and rebuttals. Using the scheme for arguments from expert opinion as an example, this paper evaluates and compares these two models of critical questions from the perspective of argumentation theory and competing legal theories about proof standardsfor defeating presumptions. The applicable proof standard is found to be a legal issue subject to argument. Verheij’smodel is shown to have problems because the proof stan-dards it applies to different kinds of ...


Climbing Up Cloud Nine: Performance Enhancement Techniques For Cloud Computing Environments, Mohamed Abusharkh 2016 The University of Western Ontario

Climbing Up Cloud Nine: Performance Enhancement Techniques For Cloud Computing Environments, Mohamed Abusharkh

Electronic Thesis and Dissertation Repository

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on ...


Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam AM Capretz, Luke Seewald 2016 Western University

Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is ...


Analysis On Alergia Algorithm: Pattern Recognition By Automata Theory, Xuanyi Qi 2016 San Jose State University

Analysis On Alergia Algorithm: Pattern Recognition By Automata Theory, Xuanyi Qi

Master's Projects

Based on Kolmogorov Complexity, a finite set x of strings has a pattern if the set x can be output by a Turing machine of length that is less than minimum of all |x|; this Turing machine, that may not be unique, is called a pattern of the finite set of string. In order to find a pattern of a given finite set of strings (assuming such a pattern exists), the ALERGIA algorithm is used to approximate such a pattern (Turing machine) in terms of finite automata. Note that each finite automaton defines a partition on formal language Σ*, ALERGIA ...


Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le 2016 San Jose State University

Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le

Master's Projects

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper ...


Dna Analysis Using Grammatical Inference, Cory Cook 2016 San Jose State University

Dna Analysis Using Grammatical Inference, Cory Cook

Master's Projects

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA.

An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm.

Testing shows that the accuracy of inferred languages for ...


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