Artificial Intelligence and Robotics Commons^™

Research Agenda Into Human-Intelligence/Machine-Intelligence Governance, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

Since the birth of modern artificial intelligence (AI) at the 1956 Dartmouth Conference, the AI community has pursued modeling and coding of human intelligence into AI reasoning processes (HI Þ MI). The Dartmouth Conference's fundamental assertion was that every aspect of human learning and intelligence could be so precisely described that it could be simulated in AI. With the exception of knowledge specific areas (such as IBM's Big Blue and a few others), sixty years later the AI community is not close to coding global human intelligence into AI. In parallel, the knowledge management (KM) community has pursued understanding of …

Metalogic Notes, Saverio Perugini

Computer Science Working Papers

A collection of notes, formulas, theorems, postulates and terminology in symbolic logic, syntactic notions, semantic notions, linkages between syntax and semantics, soundness and completeness, quantified logic, first-order theories, Goedel's First Incompleteness Theorem and more.

Statistics Notes, Saverio Perugini

Computer Science Working Papers

A collection of terms, definitions, formulas and explanations about statistics.

A Taste Of Armageddon: When Warring Is Done By Drones And Robots, Brian Stiltner

Philosophy, Theology and Religious Studies Faculty Publications

Discusses the increasing use of drones and weaponized robots. Argues that the international community must put firm ethical guidelines in place before the technology becomes rampant.

The Impact Of Increased Optimization Problem Dimensionality On Cultural Algorithm Performance, Yang Yang

Wayne State University Theses

ABSTRACT

The Impact of Increased Optimization Problem Dimensionality on

Cultural Algorithm Performance

by

Yang Yang

August 2015

Advisor: Dr. Robert Reynolds

Major: Computer Science

Degree: Master of Science

In this thesis, we investigate the performance of Cultural Algorithms when dealing with the increasing dimensionality of optimization problems. The research is based on previous cultural algorithm approaches with the Cultural Algorithms Toolkit, CAT 2.0, which supports a variety of co-evolutionary features at both the knowledge and population levels. In this project, the system was applied to the solution of 60 randomly generated problems that ranged from 2-dimensional to 5-dimensional problem spaces. …

Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas

Open Access Theses & Dissertations

Every year thousands of people are involved in traffic accidents, some of which are fatal. An important percentage of these fatalities are caused by human error, which could be prevented by increasing the awareness of drivers and the autonomy of vehicles. Since driver assistance systems have the potential to positively impact tens of millions of people, the purpose of this research is to study the micro-Doppler characteristics of vulnerable urban traffic components, i.e. pedestrians and bicyclists, based on information obtained from radar backscatter, and to develop a classification technique that allows automatic target recognition with a vehicle integrated system. For …

The Winograd Schema Challenge And Reasoning About Correlation, Dan Bailey, Amelia Harrison, Yuliya Lierler, Vladimir Lifschitz, Julian Michael

Computer Science Faculty Proceedings & Presentations

The Winograd Schema Challenge is an alternative to the Turing Test that may provide a more meaningful measure of machine intelligence. It poses a set of coreference resolution problems that cannot be solved without human-like reasoning. In this paper, we take the view that the solution to such problems lies in establishing discourse coherence. Specifically, we examine two types of rhetorical relations that can be used to establish discourse coherence: positive and negative correlation. We introduce a framework for reasoning about correlation between sentences, and show how this framework can be used to justify solutions to some Winograd Schema problems.

Consistency Checking Of Natural Language Temporal Requirements Using Answer-Set Programming, Wenbin Li

Theses and Dissertations--Computer Science

Successful software engineering practice requires high quality requirements. Inconsistency is one of the main requirement issues that may prevent software projects from being success. This is particularly onerous when the requirements concern temporal constraints. Manual checking whether temporal requirements are consistent is tedious and error prone when the number of requirements is large. This dissertation addresses the problem of identifying inconsistencies in temporal requirements expressed as natural language text. The goal of this research is to create an efficient, partially automated, approach for checking temporal consistency of natural language requirements and to minimize analysts' workload.

The key contributions of this …

Blurring And Deblurring Digital Images Using The Dihedral Group, Husein Hadi Abbas Jassim, Zahir M. Hussain, Hind R.M. Shaaban, Kawther B.R. Al-Dbag

Research outputs 2014 to 2021

A new method of blurring and deblurring digital images is presented. The approach is based on using new filters generating from average filter and H-filters using the action of the dihedral group. These filters are called HB-filters; used to cause a motion blur and then deblurring affected images. Also, enhancing images using HB-filters is presented as compared to other methods like Average, Gaussian, and Motion. Results and analysis show that the HB-filters are better in peak signal to noise ratio (PSNR) and RMSE.

Using Wild I.D. As A Reliable Source For Mark And Recapture Studies On Northern Pike (Esox Lucius), Martin Evans

Journal of Earth and Life Science

Mark and recapture studies are a very popular method fisheries biologists use to assess certain fish populations in lakes. This process can be very labor intensive and expensive. Wild I.D. is free software developed by Dartmouth College that uses SIFT program to find unique features in photographs. Initially developed for identification of African land mammals the program gives each photo a score and percent match to other photos. Northern pike were used in this study to determine if the program can recognize simulated recapture events. Photos of sample fish were taken at two separate locations, the photos were then copied …

Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith

Masters Theses

"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully …

Rice Blast Disease Forecasting For Northern Philippines, Proceso L. Fernandez Jr, Alvin R. Malicdem

Department of Information Systems & Computer Science Faculty Publications

Rice blast disease has become an enigmatic problem in several rice growing ecosystems of both tropical and temperate regions of the world. In this study, we develop models for predicting the occurrence and severity of rice blast disease, with the aim of helping to prevent or at least mitigate the spread of such disease. Data from 2 government agencies in selected provinces from northern Philippines were gathered, cleaned and synchronized for the purpose of building the predictive models. After the data synchronization, dimensionality reduction of the feature space was done, using Principal Component Analysis (PCA), to determine the most important …

Using A 3d World To Address Perceptual Issues In Human-Robot Coordination, D. Paul Benjamin, Damian M. Lyons

CSIS Technical Reports

The paper argues that effective human-robot coordination benefits from active, goal-oriented perception and internal simulation. ADAPT constructs and continually updates a 3D virtual model of the robot, its environment, and people; physics simulation allows the robot to anticipate action effects and plan interactions. The architecture also uses the virtual world to connect perceptual context, cognitive semantics, and natural-language interaction.

What Is Answer Set Programming To Propositional Satisfiability, Yuliya Lierler

Computer Science Faculty Publications

Propositional satisfiability (or satisfiability) and answer set programming are two closely related subareas of Artificial Intelligence that are used to model and solve difficult combinatorial search problems. Satisfiability solvers and answer set solvers are the software systems that find satisfying interpretations and answer sets for given propositional formulas and logic programs, respectively. These systems are closely related in their common design patterns. In satisfiability, a propositional formula is used to encode problem specifications in a way that its satisfying interpretations correspond to the solutions of the problem. To find solutions to a problem it is then sufficient to use a …

Object-Based Classification Of Earthquake Damage From High-Resolution Optical Imagery Using Machine Learning, James Bialas

Dissertations, Master's Theses and Master's Reports - Open

Object-based approaches to the segmentation and supervised classification of remotely-sensed images yield more promising results compared to traditional pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods and trial and error are often used, but time consuming and yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time sensitive applications such as earthquake induced damage assessment.

Our research takes a systematic approach to evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely-sensed imagery using Trimble’s eCognition …

Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal

Doctoral Dissertations

"Motivated by the limitations of the current reinforcement learning and optimal control techniques, this dissertation proposes quantum theory inspired algorithms for learning and control of both single-agent and multi-agent stochastic systems.

A common problem encountered in traditional reinforcement learning techniques is the exploration-exploitation trade-off. To address the above issue an action selection procedure inspired by a quantum search algorithm called Grover's iteration is developed. This procedure does not require an explicit design parameter to specify the relative frequency of explorative/exploitative actions.

The second part of this dissertation extends the powerful adaptive critic design methodology to solve finite horizon stochastic optimal …

Robotics And The Lessons Of Cyberlaw, Ryan Calo

Articles

Two decades of analysis have produced a rich set of insights as to how the law should apply to the Internet’s peculiar characteristics. But, in the meantime, technology has not stood still. The same public and private institutions that developed the Internet, from the armed forces to search engines, have initiated a significant shift toward developing robotics and artificial intelligence.

This Article is the first to examine what the introduction of a new, equally transformative technology means for cyberlaw and policy. Robotics has a different set of essential qualities than the Internet and accordingly will raise distinct legal issues. Robotics …

Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li

Electrical & Computer Engineering Faculty Publications

We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l₁/l_q regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear …

Learning Emotions: A Software Engine For Simulating Realistic Emotion In Artificial Agents, Douglas Code

Senior Independent Study Theses

This paper outlines a software framework for the simulation of dynamic emotions in simulated agents. This framework acts as a domain-independent, black-box solution for giving actors in games or simulations realistic emotional reactions to events. The emotion management engine provided by the framework uses a modified Fuzzy Logic Adaptive Model of Emotions (FLAME) model, which lets it manage both appraisal of events in relation to an individual’s emotional state, and learning mechanisms through which an individual’s emotional responses to a particular event or object can change over time. In addition to the FLAME model, the engine draws on the design …

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, …

Metadata And Linked Data In Word Sense Disambiguation, Matthew Corsmeier

Library Philosophy and Practice (e-journal)

Word Sense Disambiguation (WSD) can be assisted by taking advantage of the metadata embedded in the various ontologies, lexica, databases, etc… that exist in the Semantic Web. Automated processes that exploit the links already present in the Semantic Web can strengthen parsing of word senses by using user-contributed and semantically-linked data. These processes are only possible because of a commitment to interoperability and the creation of shared standards. This paper will review some of the most heavily used Linguistic Linked Open Data (LLOD) tools and models which show the most promise for using metadata to alleviate problems caused by polysemous …

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …

Designing A Portfolio Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir

Research Collection School Of Computing and Information Systems

Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …

Algorithm Selection Via Ranking, Jayadi Oentaryo Richard, Handoko Stephanus Daniel, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The abundance of algorithms developed to solve different problems has given rise to an important research question: How do we choose the best algorithm for a given problem? Known as algorithm selection, this issue has been prevailing in many domains, as no single algorithm can perform best on all problem instances. Traditional algorithm selection and portfolio construction methods typically treat the problem as a classification or regression task. In this paper, we present a new approach that provides a more natural treatment of algorithm selection and portfolio construction as a ranking task. Accordingly, we develop a Ranking-Based Algorithm Selection (RAS) …

Solving Uncertain Mdps With Objectives That Are Separable Over Instantiations Of Model Uncertainty, Yossiri Adulyasak, Pradeep Varakantham, Asrar Ahmed, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However, due to unavoidable uncertainty over models, it is difficult to obtain an exact specification of an MDP. We are interested in solving MDPs, where transition and reward functions are not exactly specified. Existing research has primarily focussed on computing infinite horizon stationary policies when optimizing robustness, regret and percentile based objectives. We focus specifically on finite horizon problems with a special emphasis on objectives that are separable over individual instantiations of model uncertainty (i.e., objectives that can be expressed as a sum over instantiations of model uncertainty): …

Toward Mobile Robots Reasoning Like Humans, Jean Oh, Arne Suppe, Felix Duvallet, Abdeslam Boularias, Luis Navarro-Serment, Martial Hebert, Anthony Stentz, Jerry Vinokurov, Oscar Romero, Christian Lebiere, Robert Dean

Research Collection School Of Computing and Information Systems

Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, and be able to communicate with human teammates in a clear and natural way. To address these challenges, we have developed an intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in the world. This paper describes recent …

Integrated Intelligence For Human-Robot Teams, Jean Oh, Et. Al.

Research Collection School Of Computing and Information Systems

With recent advances in robotics technologies and autonomous systems, the idea of human-robot teams is gaining ever-increasing attention. In this context, our research focuses on developing an intelligent robot that can autonomously perform non-trivial, but specific tasks conveyed through natural language. Toward this goal, a consortium of researchers develop and integrate various types of intelligence into mobile robot platforms, including cognitive abilities to reason about high-level missions, perception to classify regions and detect relevant objects in an environment, and linguistic abilities to associate instructions with the robot’s world model and to communicate with human teammates in a natural way. This …