Physical Sciences and Mathematics Commons^™

Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to externally applied negative (pain) signals that it must minimize. In doing so, it discovers relationships between objects observed through its sensory inputs and actions it performs on the observed objects. Observed concepts are not predefined but are emerging as a result of successful operations. For the optimum …

Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While finite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infinite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infinite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) …

Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up existing algorithms, largely due to the use of point-based methods. Performing point-based backup is a fundamental operation in state-of-the-art algorithms. We show that even a single backup step in the multi-agent setting is NP-Complete. Despite this negative worst-case result, we present an efficient and scalable optimal algorithm as well as a principled approximation scheme. The optimal algorithm exploits recent advances in the weighted CSP literature to overcome the complexity of the backup operation. The polytime approximation scheme …

Handling Concept Drift In Text Data Stream Constrained By High Labelling Cost, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee

Conference papers

In many real-world classification problems the concept being modelled is not static but rather changes over time - a situation known as concept drift. Most techniques for handling concept drift rely on the true classifications of test instances being available shortly after classification so that classifiers can be retrained to handle the drift. However, in applications where labelling instances with their true class has a high cost this is not reasonable. In this paper we present an approach for keeping a classifier up-to-date in a concept drift domain which is constrained by a high cost of labelling. We use …

Towards Finding Robust Execution Strategies For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max) have been studied extensively in the literature. However, the more realistic RCPSP/max problems — ones where durations of activities are not known with certainty – have received scant interest and hence are the main focus of the paper. Towards addressing the significant computational complexity involved in tackling RCPSP/max with durational uncertainty, we employ a local search mechanism to generate robust schedules. In this regard, we make two key contributions: (a) Introducing and studying the key properties of a new decision rule to specify start times of activities with …

A Study Of Three Artificial Neural Networks Models' Ability To Identify Emotions From Facial Images, Timothy Scott Hyde

Theses and Dissertations

Facial expressions conveying emotions are vital for human communication. They are also important in the studies of human interaction and behavioral studies. Recognition of emotions, using facial images, may provide a fast and practical approach that is noninvasive. Most previous studies of emotion recognition through facial images were based on the Facial Action Coding System (FACS). The FACS, which was developed by Ekman and Freisen in 1978, was created to identify different facial muscular actions. Previous artificial neural network-based approaches for classification of facial expressions focused on improving one particular neural network model for better accuracy. The purpose of this …

Architecture Optimization, Training Convergence And Network Estimation Robustness Of A Fully Connected Recurrent Neural Network, Xiaoyu Wang

All Dissertations

Recurrent neural networks (RNN) have been rapidly developed in recent years. Applications of RNN can be found in system identification, optimization, image processing, pattern reorganization, classification, clustering, memory association, etc.
In this study, an optimized RNN is proposed to model nonlinear dynamical systems. A fully connected RNN is developed first which is modified from a fully forward connected neural network (FFCNN) by accommodating recurrent connections among its hidden neurons. In addition, a destructive structure optimization algorithm is applied and the extended Kalman filter (EKF) is adopted as a network's training algorithm. These two algorithms can seamlessly work together to generate …

Optimization And Analysis Of A Robotic Navigational Algorithm, Derek Carlson, Joshua Brown Kramer, Faculty Advisor

John Wesley Powell Student Research Conference

The problem of robot navigation involves planning a path to move a robot from a start point to a known target point within an obstacle course. The efficiency of such an algorithm can be measured in several ways. For instance, Lumelsky and Stepanov measure the length of the path taken in terms of obstacle perimeters. Gabriely and Rimon compare their two-dimensional algorithm's efficiency to that of the optimal algorithm. Brown Kramer and Sabalka expand upon the work of Gabriely and Rimon to produce an algorithm for dimensions greater than two. The primary objective of this research was to implement improvements …

Artificial Intelligence: Soon To Be The World’S Greatest Intelligence, Or Just A Wild Dream?, Edward R. Kollett

Academic Symposium of Undergraduate Scholarship

The purpose of the paper was to examine the field of artificial intelligence. In particular, the paper focused on what has been accomplished towards the goal of making a machine that can think like a human, and the hardships that researchers in the field has faced. It also touched upon the potential outcomes of success. Why is this paper important? As computers become more powerful, the common conception is that they are becoming more intelligent. As computers become more integrated with society and more connected with each other, people again believe they are becoming smarter. Therefore, it is important that …

Coalition Formation Under Uncertainty, Daylond J. Hooper

Theses and Dissertations

Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents …

Developing An Effective And Efficient Real Time Strategy Agent For Use As A Computer Generated Force, Kurt Weissgerber

Theses and Dissertations

Computer Generated Forces (CGF) are used to represent units or individuals in military training and constructive simulation. The use of CGF significantly reduces the time and money required for effective training. For CGF to be effective, they must behave as a human would in the same environment. Real Time Strategy (RTS) games place players in control of a large force whose goal is to defeat the opponent. The military setting of RTS games makes them an excellent platform for the development and testing of CGF. While there has been significant research in RTS agent development, most of the developed agents …

Evolutionary Artificial Neural Network Weight Tuning To Optimize Decision Making For An Abstract Game, Corey M. Miller

Theses and Dissertations

Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors’ performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a …

Autonomous Satellite Operations For Cubesat Satellites, Jason Lionel Anderson

Master's Theses

In the world of educational satellites, student teams manually conduct operations daily, sending commands and collecting downlinked data. Educational satellites typically travel in a Low Earth Orbit allowing line of sight communication for approximately thirty minutes each day. This is manageable for student teams as the required manpower is minimal. The international Global Educational Network for Satellite Operations (GENSO), however, promises satellite contact upwards of sixteen hours per day by connecting earth stations all over the world through the Internet. This dramatic increase in satellite communication time is unreasonable for student teams to conduct manual operations and alternatives must be …

Designing Successful Online Courses - Part 2, Kathleen P. King

Department of Leadership, Policy, and Lifelong Learning

Once again, our major goal is to provide faculty with consistent guidance through the many instructional decisions and design steps they need to pursue in this process. This process is a fantastic opportunity to craft a virtual learning space in which people can engaging in learning beyond the constraints of time and space.

Five Strategies For Successful Writing Of Reports And Essays, Kathleen P. King

Department of Leadership, Policy, and Lifelong Learning

Many people cannot get started with their literary projects because they do not know where to start. In this brief article, I share insight from years of teaching students and professionals of all ages how to prepare professional work.

An Analysis Of Extreme Price Shocks And Illiquidity Among Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh

Research Collection School Of Computing and Information Systems

We construct an agent-based model to study the interplay between extreme price shocks and illiquidity in the presence of systematic traders known as trend followers. The agent-based approach is particularly attractive in modeling commodity markets because the approach allows for the explicit modeling of production, capacities, and storage constraints. Our study begins by using the price stream from a market simulation involving human participants and studies the behavior of various trend-following strategies, assuming initially that their participation will not impact the market. We notice an incremental deterioration in strategy performance as and when strategies deviate further and further from the …

Mythic Game Project Addition Of Artificial Intelligence And Quest System Components, Christopher Alan Ballinger

Theses Digitization Project

This study will describe the design decisions and principles behind the artificial intelligence (AI) for a multiplayer online role playing game and the use of an expert system to implement it. Mythic is an active project focused on the development of all aspects of a multiplayer online role playing game (MORPG). The goal of Mythic is to develop a system with the capacity to offer similar capabilities as current MORPG games available on the market, as well as providing new innovative features to distinguish it from other MORPG games and make it attractive to potential players.

Revelations Of Adaptive Technology Hiding In Your Operating System, Kathleen P. King

Department of Leadership, Policy, and Lifelong Learning

Pre-publication version of a chapter about the assistive technology tools and resources available for free in Windows OS and Mac OS. Introducing higher education faculty to free resources, features and programs which they can recommend to their students or perhaps use for themselves (for instance for fading eyesight or hearing). In addition, the chapter briefly shares strategies and examples of how they might be used.

The book will have an entire chapter dedicated to assistive technology as well. This is a popularized assistive technology chapter for generalist, NON special education, faculty to become acquainted with readily available and free resources. …

The Extent Of Clientelism In Irish Politics: Evidence From Classifying Dáil Questions On A Local-National Dimension, Sarah Jane Delany, Richard Sinnott, Niall O'Reilly

Conference papers

The availability of the full text of Irish parliamentary questions offers opportunities for using machine learning techniques to examine the currently much discussed role of elected representatives (TDs) in the Irish parliamentary system. Bluntly, are TDs mainly national legislators or “constituency messenger boys”? This paper presents an initial investigation into the use of automated text classification techniques to categorise parliamentary questions from 1922 up to 2008 as national or local. The approach uses a bag of words representation, standard feature reduction methods and an SVM classifier. Initial results show there is very little evidence in the corpus of parliamentary questions …

Motion In Augmented Reality Games: An Engine For Creating Plausible Physical Interactions In Augmented Reality Games, Brian Mac Namee, David Beaney, Qingqing Dong

Articles

The next generation of Augmented Reality (AR) games will require real and virtual objects to coexist in motion in immersive game environments. This will require the illusion that real and virtual objects interact physically together in a plausible way. The Motion in Augmented Reality Games (MARG) engine described in this paper has been developed to allow these kinds of game environments. The paper describes the design and implementation of the MARG engine and presents two proof-of-concept AR games that have been developed using it. Evaluations of these games have been performed and are presented to show that the MARG engine …

Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany

Conference papers

In CBR the design and selection of similarity measures is paramount. Selection can benefit from the use of exploratory visualisation- based techniques in parallel with techniques such as cross-validation ac- curacy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity mea- sures to a case base to be visualised so that system designers can explore the case base and the associated decision boundary space. We show, using a range of datasets and similarity measure types, how the idiosyncrasies of particular similarity measures can be illustrated and compared in CBTV allowing …

Toward A Theory-Based Natural Language Capability In Robots And Other Embodied Agents : Evaluating Hausser's Slim Theory And Database Semantics, Robin Kowalchuk Burk

Legacy Theses & Dissertations (2009 - 2024)

Computational natural language understanding and generation have been a goal of artificial intelligence since McCarthy, Minsky, Rochester and Shannon first proposed to spend the summer of 1956 studying this and related problems. Although statistical approaches dominate current natural language applications, two current research trends bring renewed focus on this goal. The nascent field of artificial general intelligence (AGI) seeks to evolve intelligent agents whose multi-subagent architectures are motivated by neuroscience insights into the modular functional structure of the brain and by cognitive science insights into human learning processes. Rapid advances in cognitive robotics also entail multi-agent software architectures that attempt …

Automated Interpretation Of The Tandem Mass Spectra Of Peptides Using Artificial Neural Networks, Timothy Patrick Manning

Theses

The manual interpretation of mass spectra is a complex and time consuming task. The problem of manually interpreting this data is further exacerbated by the large numbers of mass spectra which can potentially be produced in a single proteoniics experiment. This shows the need for high throughput approaches to the interpretation of mass spectra. Existing automated approaches are however error prone due to the complexity of the task. Accordingly, this thesis discusses and evaluates the application of neural networks to improving the sensitivity, specificity and robustness of current approaches to the automated interpretation of such mass spectral data.

Several neural …

Inversion Of 2d And 3d Dc Resistivity Imaging Data Forhigh Contrast Geophysical Regions Using Artificial Neuralnetworks., Ahmad Neyamadpour

Student Works (2010-2019)

In electrical resistivity imaging surveys, the field data along a profile are normally acquired as a subsurface distribution of apparent resistivity. One common method to obtain the true resistivity distribution is by inverting the apparent resistivity values. However, the inversion of DC resistivity imaging data is complex due to its non-linearity. This is especially true for regions with high resistivity contrast. For the complicated subsurface structure, especially when regions of high resistivity contrast exist, a conventional inversion technique based on least squares methods may not be able to invert the DC resistivity data with adequate accuracy. Therefore, in this study, …

Periodic Resource Reallocation In Two-Echelon Repairable Item Inventory Systems, Hoong Chuin Lau, Jie Pan, Huawei Song

Research Collection School Of Computing and Information Systems

Given an existing stock allocation in an inventory system, it is often necessary to perform reallocation over multiple time points to address inventory imbalance and maximize availability. In this paper, we focus on the situation where there are two opportunities to perform reallocation within a replenishment cycle. We derive a mathematical model to determine when and how to perform reallocation. Furthermore, we consider the extension of this model to the situation allowing an arbitrary number of reallocations. Experimental results show that the two-reallocation approach achieves better performance compared with the single-reallocation approach found in the literature. We also illustrate how …

Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany

Conference papers

Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of various selection strategies. Using sample datasets, this paper provides detailed examples of the differences between a range of selection strategies.

Svm Based Active Learning With Exploration, Patrick Lindstrom, Rong Hu, Sarah Jane Delany, Brian Mac Namee

Conference papers

No abstract provided.

Exploring The Frontier Of Uncertainty Space, Rong Hu, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee

Conference papers

We aim to investigate methods balancing exploitation with exploration in active learning to improve the performance of uncertainty sampling. Two exploration guided sampling methods are compared to uncertainty sampling on various real-life datasets from the 2010 Active Learning Challenge. Our initial experiments seems to indicate that combining exploration with uncertainty sampling improves performance on certain datasets but not all.

Egal: Exploration Guided Active Learning For Tcbr, Rong Hu, Sarah Jane Delany, Brian Mac Namee

Conference papers

The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present …

Cognitive Effort For Multi Agent Systems, Luca Longo

Conference papers

Cognitive Effort is a multi-faceted phenomenon that has suffered from an imperfect understanding, an informal use in everyday life and numerous definitions. This paper attempts to clarify the concept, along with some of the main influencing factors, by presenting a possible heuristic formalism intended to be implemented as a computational concept, and therefore be embedded in an artificial agent capable of cognitive effort-based decision support. Its applicability in the domain of Artificial Intelligence and Multi-Agent Systems is discussed. The technical challenge of this contribution is to start an active discussion towards the formalisation of Cognitive Effort and its application in …