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Articles 10831 - 10860 of 11145
Full-Text Articles in Computer Sciences
Pioneering The Personal Robotics Industry, Russell Nickerson
Pioneering The Personal Robotics Industry, Russell Nickerson
Undergraduate Review
The up and coming industry that I will be reporting about here is the personal and home robotics industry. I will show how the development cycle in the United States functions. I will then answer the question: What are the main limits that hold back this industry? The U.S. approach to robotics will be contrasted with Japan’s approach as Japan has another very well developed robotics program.
Fable: Finite Automata Based Learning Engine, Ryan Michael Jackson
Fable: Finite Automata Based Learning Engine, Ryan Michael Jackson
Theses Digitization Project
The purpose of this thesis will be to demostrate the feasibility of building a system that is capable of automatically learning how to increase performance at a task that is presented to it by means of modeling the problem with a finite automation and the learning engine of FABLE. Current work in the field of automatic learning of FA's (Finite Automation) is mostly focused on the building of the FA itself. This thesis will instead focus on the automated building of FA models to allow better decisions to be made in the future.
Maze Maps & Benchmark Problems, Nathan R. Sturtevant
Maze Maps & Benchmark Problems, Nathan R. Sturtevant
Moving AI Lab: 2D Maps and Benchmark Problems
Contains 60 maps of size 512x512 and benchmark problem sets. These maps are algorithm-generated mazes with corridor widths of 1, 2, 4, 8, 16, or 32. There are 10 maps and problem sets for each corridor size.
Room Maps & Benchmark Problems, Nathan R. Sturtevant
Room Maps & Benchmark Problems, Nathan R. Sturtevant
Moving AI Lab: 2D Maps and Benchmark Problems
Contains 40 maps of size 512x512 and problem sets. Maps are divided into rooms of size 8x8, 16x16, 32x32, and 64x64. There are 10 maps and problem sets for each room size. Maps with differing room sizes are not scaled: thickness of walls and passages differs.
Random Obstacle Maps & Benchmark Problems, Nathan R. Sturtevant
Random Obstacle Maps & Benchmark Problems, Nathan R. Sturtevant
Moving AI Lab: 2D Maps and Benchmark Problems
Contains 70 maps of size 512x512 and benchmark problem sets. These maps are algorithm-generated by blocking grid cells. Maps contain 10%, 15%, 20%, 25%, 30%, 35%, or 40% blocked cells. There are 10 maps and problem sets for each percentage.
The Good, The Bad And The Incorrectly Classified: Profiling Cases For Case-Base Editing, Sarah Jane Delany
The Good, The Bad And The Incorrectly Classified: Profiling Cases For Case-Base Editing, Sarah Jane Delany
Conference papers
Case-based approaches to classification, as instance-based learning techniques, have a particular reliance on training examples that other supervised learning techniques do not have. In this paper we present the RDCL case profiling technique that categorises each case in a case-base based on its classification by the case-base, the benefit it has and/or the damage it causes by its inclusion in the case-base. We show how these case profiles can identify the cases that should be removed from a case-base in order to improve generalisation accuracy and we show what aspects of existing noise reduction algorithms contribute to good performance and …
Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta
Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta
Legacy Theses & Dissertations (2009 - 2024)
We implement and evaluate a machine learning approach to improve systems for searching a database of music via melodic sample. We explore symbolic and aural input queries and test our prototypes with extensive user surveys. Our main contribution is to combine the following four elements. First is to create a unique musical abstraction that accounts for both pitch and rhythm in a relative manner. Second, our system allows for approximate matching of imperfect queries via the utilization of the Smith-Waterman algorithm that was originally designed for approximate matching of molecular subsequences, such as DNA samples. Third is to design our …
Widening The Evaluation Net, Brian Mac Namee, Mark Dunne
Widening The Evaluation Net, Brian Mac Namee, Mark Dunne
Conference papers
Intelligent Virtual Agent (IVA) systems are notoriously difficult to evaluate, particularly due to the subjectivity involved. From the various efforts to develop standard evaluation schemes for IVA systems the scheme proposed by Isbister & Doyle, which evaluates systems across five categories, seems particularly appropriate. To examine how these categories are being used, the evaluations presented in the proceedings of IVA '07 and IVA '08 are summarised and the extent to which the five categories in the Isbister & Doyle scheme are used is highlighted. Finally, to illustrate how the full scheme can be used, an evaluation of an IVA system …
Sampling With Confidence: Using K-Nn Confidence Measures In Active Learning, Rong Hu, Sarah Jane Delany, Brian Macnamee
Sampling With Confidence: Using K-Nn Confidence Measures In Active Learning, Rong Hu, Sarah Jane Delany, Brian Macnamee
Conference papers
Active learning is a process through which classifiers can be built from collections of unlabelled examples through the cooperation of a human oracle who can label a small number of examples selected as most informative. Typically the most informative examples are selected through uncertainty sampling based on classification scores. However, previous work has shown that, contrary to expectations, there is not a direct relationship between classification scores and classification confidence. Fortunately, there exists a collection of particularly effective techniques for building measures of classification confidence from the similarity information generated by k-NN classifiers. This paper investigates using these confidence measures …
Forked:A Demonstration Of Physics Realism In Augmented Reality, David Beaney, Brian Mac Namee
Forked:A Demonstration Of Physics Realism In Augmented Reality, David Beaney, Brian Mac Namee
Conference papers
In making fully immersive augmented reality (AR) applications, real and virtual objects will have to be seen to physically interact together in a realistic and believable way. This paper describes Forked! a system that has been developed to show how physical interactions between real and virtual objects can be simulated re- alistically and believably through appropriate use of a physics en- gine. The system allows users control a robotic forklift to manipu- late virtual crates in an AR environment. The paper also describes a evaluation experiment in which it is shown that the physical inter- actions between the forklift and …
Stepping Off The Stage, Brian Mac Namee, John D. Kelleher
Stepping Off The Stage, Brian Mac Namee, John D. Kelleher
Conference papers
Mixed-reality virtual agents are an attractive solution to the problems associated with human-robot interaction, allowing all the expressiveness of virtual characters to be married with the advantages of a physical artifact which exists in a shared environment with the user. However, common approaches to achieving this restrict the virtual characters appearing on top of, or encompassing the robot. This paper describes the Stepping Off the Stage system in which mixed-reality agents are allowed to step off the robot stage and move to other parts of the environment, offering compelling new interaction possibilities.
Ontology-Based Business Process Customization For Composite Web Services, Qianhui (Althea) Liang, Xindong Wu, E. K. Park, T. Khoshgoftaar, C. Chi
Ontology-Based Business Process Customization For Composite Web Services, Qianhui (Althea) Liang, Xindong Wu, E. K. Park, T. Khoshgoftaar, C. Chi
Research Collection School Of Computing and Information Systems
A key goal of the Semantic Web is to shift social interaction patterns from a producer-centric paradigm to a consumer-centric one. Treating customers as the most valuable assets and making the business models work better for them are at the core of building successful consumer-centric business models. It follows that customizing business processes constitutes a major concern in the realm of a knowledge-pull-based human semantic Web. This paper conceptualizes the customization of service-based business processes leveraging the existing knowledge of Web services and business processes. We represent this conceptualization as a new Extensible Markup Language (XML) markup language Web Ontology …
Integrated Resource Allocation And Scheduling In Bidirectional Flow Shop With Multi-Machine And Cos Constraints, Hoong Chuin Lau, Zhengyi Zhao, Shuzhi Sam Ge
Integrated Resource Allocation And Scheduling In Bidirectional Flow Shop With Multi-Machine And Cos Constraints, Hoong Chuin Lau, Zhengyi Zhao, Shuzhi Sam Ge
Research Collection School Of Computing and Information Systems
An integer programming (IP) model is proposed for integrated resource allocation and operation scheduling for a multiple job-agents system. Each agent handles a specific job-list in a bidirectional flowshop. For the individual agent scheduling problem, a formulation is proposed in continuous time domain and compared with an IP formulation in discrete time domain. Of particular interest is the formulation of the machine utilization function-- both in continuous time and discrete time. Fast heuristic methods are proposed with the relaxation of the machine capacity. For the integrated resource allocation and scheduling problem, a linear programming relaxation approach is applied to solve …
A Fuzzy Hierarchical Decision Model And Its Application In Networking Datacenters And In Infrastructure Acquisitions And Design, Michael Khader
A Fuzzy Hierarchical Decision Model And Its Application In Networking Datacenters And In Infrastructure Acquisitions And Design, Michael Khader
Walden Dissertations and Doctoral Studies
According to several studies, an inordinate number of major business decisions to acquire, design, plan, and implement networking infrastructures fail. A networking infrastructure is a collaborative group of telecommunications systems providing services needed for a firm's operations and business growth. The analytical hierarchy process (AHP) is a well established decision-making process used to analyze decisions related to networking infrastructures. AHP is concerned with decomposing complex decisions into a set of factors and solutions. However, AHP has difficulties in handling uncertainty in decision information. This study addressed the research question of solutions to AHP deficiencies. The solutions were accomplished through the …
Locating And Tracking Objects By Efficient Comparison Of Real And Predicted Synthetic Video Imagery, Damian M. Lyons, D. Paul Benjamin
Locating And Tracking Objects By Efficient Comparison Of Real And Predicted Synthetic Video Imagery, Damian M. Lyons, D. Paul Benjamin
CSIS Technical Reports
The paper uses a 3D simulation engine to generate predicted imagery for tracking objects with complex behavior and compares that imagery against real video. Salient points and an affine transformation align real and synthetic scenes so that synchronization differences are suppressed and meaningful content differences are emphasized.
Event-Detecting Multi-Agent Mdps: Complexity And Constant-Factor Approximation, Akshat Kumar, S. Zilberstein
Event-Detecting Multi-Agent Mdps: Complexity And Constant-Factor Approximation, Akshat Kumar, S. Zilberstein
Research Collection School Of Computing and Information Systems
Planning under uncertainty for multiple agents has grown rapidly with the development of formal models such as multi-agent MDPs and decentralized MDPs. But despite their richness, the applicability of these models remains limited due to their computational complexity. We present the class of event-detecting multi-agent MDPs (eMMDPs), designed to detect multiple mobile targets by a team of sensor agents. We show that eMMDPs are NP-Hard and present a scalable 2-approximation algorithm for solving them using matroid theory and constraint optimization. The complexity of the algorithm is linear in the state-space and number of agents, quadratic in the horizon, and exponential …
The Price Of Stability In Selfish Scheduling Games, Lucas Agussurja, Hoong Chuin Lau
The Price Of Stability In Selfish Scheduling Games, Lucas Agussurja, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Game theory has gained popularity as an approach to analysing and understanding distributed systems with self-interested agents. Central to game theory is the concept of Nash equilibrium as a stable state (solution) of the system, which comes with a price − the loss in efficiency. The quantification of the efficiency loss is one of the main research concerns. In this paper, we study the quality and computational characteristics of the best Nash equilibrium in two selfish scheduling models: the congestion model and the sequencing model. In particular, we present the following results: (1) In the congestion model: first, the best …
Grey Measurement Based On Rough Set Granule Calculations, Jian Liu, Zhili Huang, Shunxiang Wu, Ruiyi Chen
Grey Measurement Based On Rough Set Granule Calculations, Jian Liu, Zhili Huang, Shunxiang Wu, Ruiyi Chen
Electrical and Computer Engineering Faculty Research & Creative Works
This paper put the Rough set methodology based on the information table disposal to expand the dual (binary) relations described by the neighborhood system. To make full use of logic operations including AND operation by-bit, XOR operation by-bit and NOR operation by-bit, gains the certain and uncertain information among the various decision-making factors. The use of knowledge related with Grey system theory, establishes their mathematical model for decision making and data mining, according to the principle of the priority of certain information in decision-making and the ambiguity degree from small to big. At last, a new method for data mining …
An Analysis Of Entries In The First Tac Market Design Competition, Jinzhong Niu, Kai Cai, Peter Mcburney, Simon Parsons
An Analysis Of Entries In The First Tac Market Design Competition, Jinzhong Niu, Kai Cai, Peter Mcburney, Simon Parsons
Publications and Research
This paper presents an analysis of entries in the first TAC Market Design Competition final that compares the entries across several scenarios. The analysis complements previous work analyzing the 2007 competition, demonstrating some vulnerabilities of entries that placed highly in the competition. The paper also suggests a simple strategy that would have performed well.
Distributing Complementary Resources Across Multiple Periods With Stochastic Demand, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau
Distributing Complementary Resources Across Multiple Periods With Stochastic Demand, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments, we show that under stochastic conditions the performance variation of the process decreases as the time frame length (time …
Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan
Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
We propose, theorize and implement the Recursive Pattern-based Hybrid Supervised (RPHS) learning algorithm. The algorithm makes use of the concept of pseudo global optimal solutions to evolve a set of neural networks, each of which can solve correctly a subset of patterns. The pattern-based algorithm uses the topology of training and validation data patterns to find a set of pseudo-optima, each learning a subset of patterns. It is therefore well adapted to the pattern set provided. We begin by showing that finding a set of local optimal solutions is theoretically equivalent, and more efficient, to finding a single global optimum …
Video Event Detection Using Motion Relativity And Visual Relatedness, Feng Wang, Yu-Gang Jiang, Chong-Wah Ngo
Video Event Detection Using Motion Relativity And Visual Relatedness, Feng Wang, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Event detection plays an essential role in video content analysis. However, the existing features are still weak in event detection because: i) most features just capture what is involved in an event or how the event evolves separately, and thus cannot completely describe the event; ii) to capture event evolution information, only motion distribution over the whole frame is used which proves to be noisy in unconstrained videos; iii) the estimated object motion is usually distorted by camera movement. To cope with these problems, in this paper, we propose a new motion feature, namely Expanded Relative Motion Histogram of Bag-ofVisual-Words …
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
We consider the Resource-Constrained Project Scheduling Problem with minimal and maximal time lags under resource and duration uncertainties. To manage resource uncertainties, we build upon the work of Lambrechts et al 2007 and develop a method to analyze the effect of resource breakdowns on activity durations. We then extend the robust local search framework of Lau et al 2007 with additional considerations on the impact of unexpected resource breakdowns to the project makespan, so that partial order schedules (POS) can absorb both resource and duration uncertainties. Experiments show that our proposed model is capable of addressing the uncertainty of resources, …
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
Research Collection School Of Computing and Information Systems
Through empirical comparison of classical job shop problems (JSP) with multi-machine consideration, we find that the objective to minimize the sum of weighted tardiness has a better wait property compared with the objective to minimize the makespan. Further, we test the proposed Iterative Minimization Micro-model (IMM) heuristic method with the mixed integer programming (MIP) solution by CPLEX. For multi-machine problems, the IMM heuristic method is faster and achieves a better solution. Finally, for a large problem instance with 409 jobs and 30 types of machines, IMM-heuristic method is compared with ProModel and we find that the heuristic method is slightly …
A Secure Group Communication Architecture For Autonomous Unmanned Aerial Vehicles, Adrian N. Phillips, Barry E. Mullins, Richard Raines, Rusty O. Baldwin
A Secure Group Communication Architecture For Autonomous Unmanned Aerial Vehicles, Adrian N. Phillips, Barry E. Mullins, Richard Raines, Rusty O. Baldwin
Faculty Publications
This paper investigates the application of a secure group communication architecture to a swarm of autonomous unmanned aerial vehicles (UAVs). A multicast secure group communication architecture for the low earth orbit (LEO) satellite environment is evaluated to determine if it can be effectively adapted to a swarm of UAVs and provide secure, scalable, and efficient communications. The performance of the proposed security architecture is evaluated with two other commonly used architectures using a discrete event computer simulation developed using MATLAB. Performance is evaluated in terms of the scalability and efficiency of the group key distribution and management scheme when the …
Relationship Preserving Auction For Repeated E-Procurement, Park J., Lee J., Lau H.
Relationship Preserving Auction For Repeated E-Procurement, Park J., Lee J., Lau H.
Research Collection School Of Computing and Information Systems
While e-procurement auction has helped firms to achieve lower procurement costs, auction mechanisms that prevail at present in procurement markets need to address an important issue that concerns the ability to maintain long term relationships with the partners, especially in repeated e-procurement settings. In this paper, we propose a Relationship Preserving Auction (RPA) mechanism that augments the conventional auction mechanism with a bidder relationship scoring model. Our proposed mechanism gives increased chances of winning to the bidders who have bidden at relatively competitive price but had comparatively less wins so far. Keeping these bidders in the auction over time will …
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson
Faculty Publications
This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. This paper describes two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ant colony system algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions …
A Simplex Model For Layered Niche Networks, Philip Fraundorf
A Simplex Model For Layered Niche Networks, Philip Fraundorf
Physics Faculty Works
No abstract provided.
H-Dpop: Using Hard Constraints For Search Space Pruning In Dcop, Akshat Kumar, Adrian Petcu, Boi Faltings
H-Dpop: Using Hard Constraints For Search Space Pruning In Dcop, Akshat Kumar, Adrian Petcu, Boi Faltings
Research Collection School Of Computing and Information Systems
In distributed constraint optimization problems, dynamic programming methods have been recently proposed (e.g. DPOP). In dynamic programming many valuations are grouped together in fewer messages, which produce much less networking overhead than search. Nevertheless, these messages are exponential in size. The basic DPOP always communicates all possible assignments, even when some of them may be inconsistent due to hard constraints. Many real problems contain hard constraints that significantly reduce the space of feasible assignments. This paper introduces H-DPOP, a hybrid algorithm that is based on DPOP, which uses Constraint Decision Diagrams (CDD) to rule out infeasible assignments, and thus compactly …
Linear Relaxation Techniques For Task Management In Uncertain Settings, Pradeep Varakantham, Stephen F. Smith
Linear Relaxation Techniques For Task Management In Uncertain Settings, Pradeep Varakantham, Stephen F. Smith
Research Collection School Of Computing and Information Systems
In this paper, we consider the problem of assisting a busy user in managing her workload of pending tasks. We assume that our user is typically oversubscribed, and is invariably juggling multiple concurrent streams of tasks (or work flows) of varying importance and urgency. There is uncertainty with respect to the duration of a pending task as well as the amount of follow-on work that may be generated as a result of executing the task. The user’s goal is to be as productive as possible; i.e., to execute tasks that realize the maximum cumulative payoff. This is achieved by enabling …