Physical Sciences and Mathematics Commons^™

Solution Pluralism And Metaheuristics, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, Frederic H. Murphy, David Harlan Wood

Research Collection School Of Computing and Information Systems

Solution pluralism is an approach to problem solving and deliberation. It employs a plurality of distinct solutions for a decision problem for aiding decision making. The concept is well established in existing practice, although perhaps not recognized as such. This paper: (1) presents the concept as a generalization of established practice, (2) briefly describes successful uses of the concept in practice, and (3) presents several areas that appear would benefit from application of the concept. Throughout, the role of metaheuristics in finding the pluralities of solutions is emphasized.

Learning Author’S Writing Pattern System By Automata, Qun Yu

Master's Projects

The purpose of the report is to document our project’s theory, implementation and test results. The project works on an automata-based learning system which models authors’ writing characters with automatons. Since there were pervious works done by Dr. T.Y. Lin and Ms. S.X. Zhang, we continue on ALERGIA algorithm analysis and initial common pattern study in this project. Although every author has his/her own writing style, such as sentence length and word frequency etc, there are always some similarities in writing style. We hypothesize that common strings fogged the expected test result, just like the noise in radio wave. This …

A Novel Methodology For Evaluating User Interfaces In Health Care, Luca Longo, Bridget Kane

Conference papers

A pilot study is reported to identify an improved method of evaluating digital user interfaces in health care. Experience and developments from the aviation industry and the NASA-TLX mental workload assessment tools are applied in conjunction with Nielsen heuristics for evaluating an Electronic Health Record System in an Irish hospital. The NASA-TLX performs subjective workload assessments on operators working with various human-computer systems. Results suggest that depending on the cognitive workload and the working context of users, the usability will differ for the same digital interface. We conclude that incorporating the NASA-TLX with Nielsen's heuristics offers a more reliable method …

Effects Of Appearance And Functions On Likability And Perceived Occupational Suitability Of Robots, Sau-Lai Lee, Ivy Yee-Man Lau, Ying-Yi Hong

Research Collection School of Social Sciences

This article reports three experiments that examined the association between (a) appearances and perceived capabilities of robots, (b) appearance and capabilities of robots and liking for the robots, and (c) perceived capabilities of robots and judgments concerning their suitability for different occupations. In Experiment 1, the authors found that participants perceived human- and animal-like robots to have relatively more warmth-related (e.g., emotion) capabilities than machinelike robots have. In Experiment 2, the authors found that liking for robots was not affected by their human likeness or their having warmth or competence capabilities. In Experiment 3, participants generally thought that robots should …

Intelligent Systems Development In A Non Engineering Curriculum, Emily A. Brand, William L. Honig, Matthew Wojtowicz

Computer Science: Faculty Publications and Other Works

Much of computer system development today is programming in the large - systems of millions of lines of code distributed across servers and the web. At the same time, microcontrollers have also become pervasive in everyday products, economical to manufacture, and represent a different level of learning about system development. Real world systems at this level require integrated development of custom hardware and software.

How can academic institutions give students a view of this other extreme - programming on small microcontrollers with specialized hardware? Full scale system development including custom hardware and software is expensive, beyond the range of any …

Multiagent Coalition Formation In Uncertain Environments With Type-Changing Influences And Its Application Towards Forming Human Coalitions, Nobel A. Khandaker

School of Computing: Dissertations, Theses, and Student Research

We aim to solve the problem forming multiagent coalitions in uncertain environments where the coalition members’ capability of solving tasks change due to their learning. The MCFP-Mproblem for the agents refers to forming or joining coalitions on behalf of a set of human users so that those human users can solve tasks and improve their types (expertise) to improve their performances over time. MCFP-A problem for a set of agents refers to their forming or joining coalitions so that they are able to solve a set of assigned tasks while optimize their performance over time. We propose the Integrated Human …

Message-Passing Algorithms For Large Structured Decentralized Pomdps, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDPs provide a rigorous framework for multi-agent decision-theoretic planning. However, their high complexity has limited scalability. In this work, we present a promising new class of algorithms based on probabilistic inference for infinite-horizon ND-POMDPs---a restricted Dec-POMDP model. We first transform the policy optimization problem to that of likelihood maximization in a mixture of dynamic Bayes nets (DBNs). We then develop the Expectation-Maximization (EM) algorithm for maximizing the likelihood in this representation. The EM algorithm for ND-POMDPs lends itself naturally to a simple message-passing paradigm guided by the agent interaction graph. It is thus highly scalable w.r.t. the number of …

Distributed Model Shaping For Scaling To Decentralized Pomdps With Hundreds Of Agents, Prasanna Velagapudi, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri

Research Collection School Of Computing and Information Systems

The use of distributed POMDPs for cooperative teams has been severely limited by the incredibly large joint policy- space that results from combining the policy-spaces of the individual agents. However, much of the computational cost of exploring the entire joint policy space can be avoided by observing that in many domains important interactions between agents occur in a relatively small set of scenarios, previously defined as coordination locales (CLs) [11]. Moreover, even when numerous interactions might occur, given a set of individual policies there are relatively few actual interactions. Exploiting this observation and building on an existing model shaping algorithm, …

Noise, Delays, And Resonance In A Neural Network, Austin Quan

HMC Senior Theses

A stochastic-delay differential equation (SDDE) model of a small neural network with recurrent inhibition is presented and analyzed. The model exhibits unexpected transient behavior: oscillations that occur at the boundary of the basins of attraction when the system is bistable. These are known as delay-induced transitory oscillations (DITOs). This behavior is analyzed in the context of stochastic resonance, an unintuitive, though widely researched phenomenon in physical bistable systems where noise can play in constructive role in strengthening an input signal. A method for modeling the dynamics using a probabilistic three-state model is proposed, and supported with numerical evidence. The potential …

Decentralized Decision Support For An Agent Population In Dynamic And Uncertain Domains, Pradeep Reddy Varakantham, Shih-Fen Cheng, Thi Duong Nguyen

Research Collection School Of Computing and Information Systems

This research is motivated by problems in urban transportation and labor mobility, where the agent flow is dynamic, non-deterministic and on a large scale. In such domains, even though the individual agents do not have an identity of their own and do not explicitly impact other agents, they have implicit interactions with other agents. While there has been much research in handling such implicit effects, it has primarily assumed controlled movements of agents in static environments. We address the issue of decision support for individual agents having involuntary movements in dynamic environments . For instance, in a taxi fleet serving …

Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato

Research Collection School Of Computing and Information Systems

In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …

A Simple Curious Agent To Help People Be Curious, Han Yu, Zhiqi Shen, Chunyan Miao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Curiosity is an innately rewarding state of mind that, over the millennia, has driven the human race to explore and discover. Many researches in pedagogical science have confirmed the importance of being curious to the students' cognitive development. However, in the newly popular virtual world-based learning environments (VLEs), there is currently a lack of attention being paid to enhancing the learning experience by stimulating the learners' curiosity. In this paper, we propose a simple model for curious agents (CAs) which can be used to stimulate learners' curiosity in VLEs. Potential future research directions will be discussed.

Incremental Dcop Search Algorithms For Solving Dynamic Dcop Problems, William Yeoh, Pradeep Varakantham, Xiaoxun Sun, Sven Koenig

Research Collection School Of Computing and Information Systems

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper …

Random Keys Genetic Algorithms Scheduling And Rescheduling Systems For Common Production Systems, Elkin Rodriguez-Velasquez

Engineering Management & Systems Engineering Theses & Dissertations

The majority of scheduling research deals with problems in specific production environments with specific objective functions. However, in many cases, more than one problem type and/or objective function exists, resulting in the need for a more generic and flexible system to generate schedules. Furthermore, most of the published scheduling research focuses on creating an optimal or near optimal initial schedule during the planning phase. However, after production processes start, circumstances like machine breakdowns, urgent jobs, and other unplanned events may render the schedule suboptimal, obsolete or even infeasible resulting in a "rescheduling" problem, which is typically also addressed for a …

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 …

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 …

Recognition Situations Using Extended Dempster-Shafer Theory, Susan Mckeever

Other resources

Weiser’s [111] vision of pervasive computing describes a world where technology seamlessly integrates into the environment, automatically responding to peoples’ needs. Underpinning this vision is the ability of systems to automatically track the situation of a person. The task of situation recognition is critical and complex: noisy and unreliable sensor data, dynamic situations, unpredictable human behaviour and changes in the environment all contribute to the complexity. No single recognition technique is suitable in all environments. Factors such as availability of training data, ability to deal with uncertain information and transparency to the user will determine which technique to use in …

Punctuated Anytime Learning And The Xpilot-Ai Combat Environment, Phillip Fritzsche

Computer Science Honors Papers

In this paper, research is presented on an application of Punctuated Anytime Learning with Fitness Biasing, a type of computational intelligence and evolutionary learning, for real-­time learning of autonomous agents controllers in the space combat game Xpilot. Punctuated Anytime Learning was originally developed as a means of effective learning in the field of evolutionary robotics. An analysis was performed on the game environment to determine optimal environmental settings for use during learning, and Fitness Biasing is employed using this information to learn intelligent behavior for a video game agent controller in real-­time. Xpilot-­AI, an Xpilot add-­on designed for testing learning …

Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki

Electronic Theses and Dissertations

This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at …

An Exploration Of Multi-Agent Learning Within The Game Of Sheephead, Brady Brau

All Graduate Theses, Dissertations, and Other Capstone Projects

In this paper, we examine a machine learning technique presented by Ishii et al. used to allow for learning in a multi-agent environment and apply an adaptation of this learning technique to the card game Sheephead. We then evaluate the effectiveness of our adaptation by running simulations against rule-based opponents. Multi-agent learning presents several layers of complexity on top of a single-agent learning in a stationary environment. This added complexity and increased state space is just beginning to be addressed by researchers. We utilize techniques used by Ishii et al. to facilitate this multi-agent learning. We model the environment of …

Automatic Annotation Of Referring Expression In Situated Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee

Articles

To apply machine learning techniques to the production and interpretation of natural language, we need large amounts of annotated language data. Manual annotation, however, is an expensive and time consuming process since it involves human annotators looking at the data and explicitly adding information that is implicitly contained in the data, based on their judgment. This work presents an approach to automatically annotating referring expressions in situated dialogues by exploiting the interpretation of language by the participants in the dia- logue. We associate instructions concerning objects in the environment with automatically detected events involving these objects and predict the referents …

Autonomous Entropy-Based Intelligent Experimental Design, Nabin Kumar Malakar

Legacy Theses & Dissertations (2009 - 2024)

The aim of this thesis is to explore the application of probability and information theory in experimental design, and to do so in a way that combines what we know about inference and inquiry in a comprehensive and consistent manner.

Robotics In Hazardous Environments- Real Deployments By The Savannah River National Lab, Steven Tibrea, Thomas Nance, Eric Kriikku

Journal of the South Carolina Academy of Science

No abstract provided.

Imbalanced Learning For Functional State Assessment, Feng Li, Frederick Mckenzie, Jiang Li, Guanfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)

Electrical & Computer Engineering Faculty Publications

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classis and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random under-sampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving test dataset show that accuracies for minority classes …

Would Price Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh

Research Collection School Of Computing and Information Systems

On May 6, 2010, the U.S. equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (see Figure 1) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. The authors contend that the events of May 6, 2010 exhibit patterns consistent with the type of "flash crash" observed in their earlier study (2010). This paper describes the results of nine different simulations created by using a large-scale computer model to reconstruct the critical elements …

Feasibility Study Of Utility-Directed Behaviour For Computer Game Agents, Colm Sloan, John D. Kelleher, Brian Mac Namee

Conference papers

Utility-based control (UBC) hasn’t been widely adopted for commercial game AI. Some of the reasons for this are that UBC is perceived to be: (1) resource intensive, (2) difficult to design complex behaviours with, and (3) difficult to scale for use in complex environments. This paper investigates these perceptions to see if UBC is suitable for controlling the behaviour of non-player characters in commercial games. The investigation compares agents using a UBC system against two control systems that are more frequently used in commercial games: finite state machines (FSMs), considered a simple control system, and goal-oriented action planning (GOAP), considered …

An Integrated Computer-Aided Robotic System For Dental Implantation, Xiaoyan Sun, Yongki Yoon, Jiang Li, Frederic D. Mckenzie

Electrical & Computer Engineering Faculty Publications

This paper describes an integrated system for dental implantation including both preoperative planning utilizing computer-aided technology and automatic robot operation during the intra-operative stage. A novel two-step registration procedure was applied for transforming the preoperative plan to the operation of the robot, with the help of a Coordinate Measurement Machine (CMM). Experiments with a patient-specific phantom were carried out to evaluate the registration error for both position and orientation. After adopting several improvements, registration accuracy of the system was significantly improved. Sub-millimeter accuracy with the Target Registration Errors (TREs) of 0.38±0.16 mm (N=5) was achieved. The target orientation errors after …

Improving Service Through Just-In-Time Concept In A Dynamic Operational Environment, Kar Way Tan, Hoong Chuin Lau, Na Fu

Research Collection School Of Computing and Information Systems

This paper is concerned with the problem of Just-In-Time (JIT) job scheduling in a dynamic environment under uncertainty to attain timely service. We provide an approach, based on robust scheduling concepts, to analytically evaluate the expected cost of earliness and tardiness for each job and also the project. In addition, we search for a schedule execution policy with the minimum robust cost such that for a given risk level (epsilon), the actual realized schedule has (1 - epsilon) probability of completing with less than or equal to this robust cost. Our method is quite generic, and can be applied to …

Instance-Based Parameter Tuning Via Search Trajectory Similarity Clustering, Linda Lindawati, Hoong Chuin Lau, David Lo

Research Collection School Of Computing and Information Systems

This paper is concerned with automated tuning of parameters in local-search based meta-heuristics. Several generic approaches have been introduced in the literature that returns a ”one-size-fits-all” parameter configuration for all instances. This is unsatisfactory since different instances may require the algorithm to use very different parameter configurations in order to find good solutions. There have been approaches that perform instance-based automated tuning, but they are usually problem-specific. In this paper, we propose CluPaTra, a generic (problem-independent) approach to perform parameter tuning, based on CLUstering instances with similar PAtterns according to their search TRAjectories. We propose representing a search trajectory as …

Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati

Research Collection School Of Computing and Information Systems

Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter …