Artificial Intelligence and Robotics Commons^™

Using Hidden Markov Models To Detect Dna Motifs, Santrupti Nerli

Master's Projects

During the process of gene expression in eukaryotes, mRNA splicing is one of the key processes carried out by a complex called spliceosome. Spliceosome guarantees proper removal of introns and joining of exons before the translation process. Precise splicing is essential for the production of functional proteins. Spliceosome detects specific sequence motifs within an mRNA sequence called splice sites. Two of the splice sites are the 5’ and 3’ sites that border all the introns. Normal splicing process if disrupted by mutation may lead to fatal diseases. In this work, we predict splice sites in a human genome using hidden …

Using Genetic Learning In Weight-Based Game Ai, Dylan Anthony Kordsmeier

Computer Science and Computer Engineering Undergraduate Honors Theses

Human beings have been playing games for centuries, and over time, mankind has learned how to excel at these fun competitions. With the ever-growing interest in the field of Machine Learning and Artificial Intelligence (AI), developers have been finding ways to let the game compete against the player much like another human would. While there are many approaches to humanlike learning in machines, this article will focus on using Evolutionary Optimization as a method to develop different levels of pseudo-thinking inan AI used for ato effectively play the Connect Four game.

Direct: A Scalable Approach For Route Guidance In Selfish Orienteering Problems, Pradeep Varakantham, Hala Mostafa, Na Fu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of crowd congestion at venues like theme parks, museums and world expos by providing route guidance to multiple selfish users (with budget constraints) moving through the venue simultaneously. To represent these settings, we introduce the Selfish Orienteering Problem (SeOP) that combines two well studied problems from literature, namely Orienteering Problem (OP) and Selfish Routing (SR). OP is a single agent routing problem where the goal is to minimize latency (or maximize reward) in traversing a subset of nodes while respecting budget constraints. SR is a game between selfish agents looking for minimum latency routes from source …

Oscar: Online Selection Of Algorithm Portfolios With Case Study On Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper introduces an automated approach called OSCAR that combines algorithm portfolios and online algorithm selection. The goal of algorithm portfolios is to construct a subset of algorithms with diverse problem solving capabilities. The portfolio is then used to select algorithms from for solving a particular (set of) instance(s). Traditionally, algorithm selection is usually performed in an offline manner and requires the need of domain knowledge about the target problem; while online algorithm selection techniques tend not to pay much attention to a careful construction of algorithm portfolios. By combining algorithm portfolios and online selection, our hope is to design …

Adviser: A Web-Based Algorithm Portfolio Deviser, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The basic idea of algorithm portfolio [1] is to create a mixture of diverse algorithms that complement each other’s strength so as to solve a diverse set of problem instances. Algorithm portfolios have taken on a new and practical meaning today with the wide availability of multi-core processors: from an enterprise perspective, the interest is to make best use of parallel machines within the organization by running different algorithms simultaneously on different cores to solve a given problem instance. Parallel execution of a portfolio of algorithms as suggested by [2, 3] a number of years …

Near-Optimal Decentralized Power Supply Restoration In Smart Grids, Pritee Agrawal, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust operation in the face of disasters. Such heterogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, provides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, enabling decentralized …

Predicting Bundles Of Spatial Locations From Learning Revealed Preference Data, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau, Ramayya Krishnan

Research Collection School Of Computing and Information Systems

We propose the problem of predicting a bundle of goods, where the goods considered is a set of spatial locations that an agent wishes to visit. This typically arises in the tourism setting where attractions can often be bundled and sold as a package to visitors. While the problem of predicting future locations given the current and past trajectories is well-established, we take a radical approach by looking at it from an economic point of view. We view an agent's past trajectories as revealed preference (RP) data, where the choice of locations is a solution to an optimisation problem according …

Prepositional Phrase Attachment Problem Revisited: How Verbnet Can Help, Dan Bailey, Yuliya Lierler, Benjamin Susman

Computer Science Faculty Proceedings & Presentations

Resolving attachment ambiguities is a pervasive problem in syntactic analysis. We propose and investigate an approach to resolving prepositional phrase attachment that centers around the ways of incorporating semantic knowledge derived from the lexico-semantic ontologies such as VERBNET and WORDNET.

Technology And Gambling: How It Has Changed The Game, Nicholas Hensley

Senior Seminars

No abstract provided.

Reflective, Deliberative Agent-Based Information Gathering, Adam D. Eck

School of Computing: Dissertations, Theses, and Student Research

As computational devices and entities become further established as routine, omnipresent components of our everyday lives (e.g., wearable sensors, smart homes, cyber-physical systems, embodied agents, human-robot interactions), such systems face an increased pressure to perpetually understand the complex, noisy, uncertain world around them in real-time. This environmental knowledge enables computational systems to intelligently decide how to best behave in response to the current situation, adapt to the ever-changing conditions of the dynamic world, and accomplish system goals that ultimately aim to improve our daily experience. However, achieving and maintaining such knowledge is very complicated due to the complexities and challenging …

Trajectory Generation For Lane-Change Maneuver Of Autonomous Vehicles, Ashesh Goswami

Open Access Theses

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during …

Securing Communication Within The Harms Model For Use With Firefighting Robots, Maxwell D. Dewees

Open Access Theses

Humans and robots must work together in increasingly complex networks to achieve a common goal. In this research, firefighting robots are a part of a larger, decentralized system of humans, agents, robots, machines, and sensors (HARMS). Although communication in a HARMS model has been utilized in previous research, this new study looks at the security considerations of the communications layer of the HARMS model. A network attack known as a man-in-the-middle attack is successfully demonstrated in this paper. Then, a secure communications protocol is proposed to help provide confidentiality and authentication of HARMS actors. This research is applied to any …

Improving Product Design Phase For Engineer To Order (Eto) Product With Knowledge Base Engineering (Kbe), Hanhdung Thi Dinh

Open Access Theses

In industry currently Computer Aided Design (CAD) is an important tool for the modification, analysis, or optimization of the 3D virtual environment that replicates the physical product. CAD software is an efficient and reliable tool. However, as globalization increases customer demands, this process needs to be faster and more efficient to accommodate changing product design situations, especially for Engineer-to- Order (ETO) products. ^ The traditional method of product design process is to operate CAD software without argumentation. Design engineers create CAD prototypes and drawings based on available knowledge and information which comes from engineering experts, company standards, industrial practices as …

Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han

Research Collection School Of Computing and Information Systems

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.

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 …

Experimental Investigation Of Stochastic Parafoil Guidance Using A Graphics Processing Unit, Nathan Slegers, Andrew Brown, Jonathan Rogers

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

Control of autonomous systems subject to stochastic uncertainty is a challenging task. In guided airdrop applications, random wind disturbances play a crucial role in determining landing accuracy and terrain avoidance. This paper describes a stochastic parafoil guidance system which couples uncertainty propagation with optimal control to protect against wind and parameter uncertainty in the presence of impact area obstacles. The algorithm uses real-time Monte Carlo simulation performed on a graphics processing unit (GPU) to evaluate robustness of candidate trajectories in terms of delivery accuracy, obstacle avoidance, and other considerations. Building upon prior theoretical developments, this paper explores performance of the …

A Study Of Decision Process In Mcdm Problems With Large Number Of Criteria, Jian Liu, Peng Liu, Si Feng Liu, Xian Zhong Zhou, Tao Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an effective decision process method is proposed to address the challenge in a multiple criteria decision-making (MCDM) problem because of large number of criteria. This method is based on the criteria reduction, tolerance relation, and prospect theory (PT). By building a discernibility matrix for tolerance relation (DMTR) in an MCDM problem with numerical values or interval numbers, this method first allows us to recognize a set of critical criteria from a large criteria pool and ignore the other criteria. Next, it establishes the criteria weights through the DMTR as they are usually not indicated in the data. …

Using Infrastructure-Provided Context Filters For Efficient Fine-Grained Activity Sensing, Vigneshwaran Subbaraju, Sougata Sen, Archan Misra, Satyadip Chakraborty, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

While mobile and wearable sensing can capture unique insights into fine-grained activities (such as gestures and limb-based actions) at an individual level, their energy overheads are still prohibitive enough to prevent them from being executed continuously. In this paper, we explore practical alternatives to addressing this challenge-by exploring how cheap infrastructure sensors or information sources (e.g., BLE beacons) can be harnessed with such mobile/wearable sensors to provide an effective solution that reduces energy consumption without sacrificing accuracy. The key idea is that many fine-grained activities that we desire to capture are specific to certain location, movement or background context: infrastructure …

A Proposed Model For The Approach To Augmented Reality Deployment In Marketing Communications, Stephen O'Mahony

Dept. of Marketing & International Business Conference Material

In the contemporary landscape, human interaction is characterized by the pervasive mediation of intelligent agents. Owing to advances in computer science and engineering, phenomena once limited to fixed locations are metamorphosing from extraneous entities to implicit components of the everyday. Computing power has simultaneously enhanced and miniaturized to the extent that contemporary consumer devices demonstrate power equivalent to or greater than that of personal computers of recent memory. At the same time, network connectivity has proliferated to ubiquitous levels, reflected in the fact that the amount of inanimate objects connected to the Internet has recently surpassed the human population of …

A Heuristic Evolutionary Method For The Complementary Cell Suppression Problem, Hira B. Herrington

CCAC Theses and Dissertations

Cell suppression is a common method for disclosure avoidance used to protect sensitive information in two-dimensional tables where row and column totals are published along with non-sensitive data. In tables with only positive cell values, cell suppression has been demonstrated to be non-deterministic NP-hard. Therefore, finding more efficient methods for producing low-cost solutions is an area of active research.

Genetic algorithms (GA) have shown to be effective in finding good solutions to the cell suppression problem. However, these methods have the shortcoming that they tend to produce a large proportion of infeasible solutions. The primary goal of this research was …

A Defeasible Reasoning Framework For Human Mental Workload Representation And Assessment, Luca Longo

Conference papers

Human mental workload (MWL) has gained importance in the last few decades as an important design concept. It is a multifaceted complex construct mainly applied in cognitive sciences and has been defined in many different ways. Although measuring MWL has potential advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. This research contributes to the body of knowledge by providing an extensible framework built upon defeasible reasoning, and implemented with argumentation theory (AT), in which MWL can be better defined, measured, analysed, explained and applied in different human–computer interactive contexts. …

Introducing Machine Learning Via Baseball's Hall Of Fame, David Hansen

Faculty Publications - Department of Electrical Engineering and Computer Science

Machine Learning via Artificial Neural Networks (ANNs) is often introduced in a one-semester course on Artificial Intelligence. Baseball’s annual Hall of Fame election provides a simple, tractable, data-rich domain for learning how to use ANNs for predictive analytics. We describe how we use the Fast Artificial Neural Network (FANN) toolkit for a course assignment that predicts which players are likely to be elected to Baseball’s Hall of Fame.

Potential Of Cognitive Computing And Cognitive Systems, Ahmed K. Noor

Computational Modeling & Simulation Engineering Faculty Publications

Cognitive computing and cognitive technologies are game changers for future engineering systems, as well as for engineering practice and training. They are major drivers for knowledge automation work, and the creation of cognitive products with higher levels of intelligence than current smart products. This paper gives a brief review of cognitive computing and some of the cognitive engineering systems activities. The potential of cognitive technologies is outlined, along with a brief description of future cognitive environments, incorporating cognitive assistants - specialized proactive intelligent software agents designed to follow and interact with humans and other cognitive assistants across the environments. The …

Isolation In Synchronized Drone Formations, Andrew P. Brunner

Electronic Theses and Dissertations

This paper expands on a theoretical model that is used for aerial robots that are working cooperatively to complete a task. In certain situations, such as when multiple robots have catastrophic failures, the surviving robots could become isolated so that they never again communicate with another robot. We prove some properties about isolated robots flying in a grid formation, and we present an algorithm that determines how many robots need to fail to isolate at least one robot. Finally, we propose a strategy that eliminates the possibility of isolation altogether.

Facial Expression Analysis Via Transfer Learning, Xiao Zhang

Electronic Theses and Dissertations

Automated analysis of facial expressions has remained an interesting and challenging research topic in the field of computer vision and pattern recognition due to vast applications such as human-machine interface design, social robotics, and developmental psychology. This dissertation focuses on developing and applying transfer learning algorithms - multiple kernel learning (MKL) and multi-task learning (MTL) - to resolve the problems of facial feature fusion and the exploitation of multiple facial action units (AUs) relations in designing robust facial expression recognition systems. MKL algorithms are employed to fuse multiple facial features with different kernel functions and tackle the domain adaption problem …

Generating Plans In Concurrent, Probabilistic, Over-Subscribed Domains, Li Li

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

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve …

Energy Cost Forecasting For Event Venues, Katarina Grolinger, Andrea Zagar, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning …

The Value Of Diagnostic Software And Doctors' Decision Making, Babatunde Ayodele Alaofin

Walden Dissertations and Doctoral Studies

The prevalence of medical misdiagnosis has remained high despite the adoption of diagnostic software. This ongoing controversy about the role of technology in mitigating the problem of misdiagnosis centers on the question of whether diagnostic software does reduce the incidence of misdiagnosis if properly relied upon by physicians. The purpose of this quantitative, cross-sectional study based on planned behavior theory was to measure doctors' opinions of diagnostic technology's medical utility. Recruitment e-mails were sent to 3,100 AMA-accredited physicians through their database that yielded a sample of 99 physicians for the study. One-sample t tests and, where appropriate because of non-normal …