Computer Sciences Commons^™

Object Retrieval From Secure Unknown Interior Spaces Using Autonomous Unmanned Aerial Vehicles, John Levous, Julie Hoven, Victor Habgood, Abdulrahman Alotaibi, Brandon Ordway, Garibe Mohammed-Jones, Haole Guo, Filip Cuckov, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

This paper describes an autonomous unmanned aerial vehicle (UAV) designed to participate in the 23rd annual International Aerial Robotics Competition. The UAV is equipped with onboard sensors and a Harvard architecture 8-bit RISC microcontroller to monitor and locally control its flight telemetry. Additional sensors (and an additional microcontroller) are used for detecting and mapping of structural and environmental objects while the UAV is in flight. The microcontrollers are interfaced with wireless communication modules for transmitting flight telemetry and structural/environmental data to a ground control station that sends the UAV command and control signals required for the mission objectives. The UAV …

Using Monte Carlo Tree Search For Replanning In A Multistage Simultaneous Game, Daniel Beard, Philip Hingston, Martin Masek

Research outputs 2012

In this study, we introduce MC-TSAR, a Monte Carlo Tree Search algorithm for strategy selection in simultaneous multistage games. We evaluate the algorithm using a battle planning scenario in which replanning is possible. We show that the algorithm can be used to select a strategy that approximates a Nash equilibrium strategy, taking into account the possibility of switching strategies part way through the execution of the scenario in the light of new information on the progress of the battle.

A Multimodal Problem For Competitive Coevolution, Philip Hingston, Tirtha Ranjeet, Chiou Peng Lam, Martin Masek

Research outputs 2012

Coevolutionary algorithms are a special kind of evolutionary algorithm with advantages in solving certain specific kinds of problems. In particular, competitive coevolutionary algorithms can be used to study problems in which two sides compete against each other and must choose a suitable strategy. Often these problems are multimodal - there is more than one strong strategy for each side. In this paper, we introduce a scalable multimodal test problem for competitive coevolution, and use it to investigate the effectiveness of some common coevolutionary algorithm enhancement techniques.

Report On Advances In The Field Of Artificial Intelligence Attributed To Captcha, Craig M. Schow

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

A CAPTCHA is a specialized human interaction proof that exploits gaps between human and computer recognition abilities. By design, the hardness of a CAPTCHA is based on the difficulty of advancing the underlying artificial intelligence [AI] technology to a level that eliminates any exploitable gap. Due to this fact computer scientists have concluded that the widespread use of CAPTCHA would accelerate research in the underlying fields of AI eventually leading to near-­‐human capabilities in certain AI systems. Despite these predictions no attempt has been made to identify advances in AI which can be attributed to the use of CAPTCHA.

The …

Chatbots In The Library: Is It Time?, Deeann Allison

University of Nebraska-Lincoln Libraries: Faculty Publications

This paper describes a pilot at the University of Nebraska-Lincoln for a chatbot that answers questions about the library and library resources. The chatbot was developed using a SQL database to store the question and answers using Artificial Intelligence Mark-up Language metadata. The user interface was built using PHP, adapted from Program-O. The open source PHP program was modified to support better display and the launching of URLs within the chatbot screen. Database content was created by “mining” library websites for information, and analyzing chat logs.

The chatbot answers questions from a variety of users from around the world. It …

Partitioning Of Minimotifs Based On Function With Improved Prediction Accuracy, Sanguthevar Rajasekaran, Tian Mi, Jerlin Camilus Merlin, Aaron Oommen, Patrick R. Gradie, Martin R. Schiller

Life Sciences Faculty Research

Background

Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions.

Methodology/Principal Findings

Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have …

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 …

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 …

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 …

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.

A Computational Analysis Of Cognitive Effort, Luca Longo, Stephen Barrett

Books/Book Chapters

Cognitive effort is a concept of unquestionable utility in understanding human behaviour. However, cognitive effort has been defined in several ways in literature and in everyday life, suffering from a partial understanding. It is common to say “Pay more attention in studying that subject” or “How much effort did you spend in resolving that task?”, but what does it really mean? This contribution tries to clarify the concept of cognitive effort, by introducing its main influencing factors and by presenting a formalism which provides us with a tool for precise discussion. The formalism is implementable as a computational concept and …

Redtnet: A Network Model For Strategy Games, Philip Hingston, Mike Preuss, Daniel Spierling

Research outputs pre 2011

In this work, we develop a simple, graph-based framework, RedTNet, for computational modeling of strategy games and simulations. The framework applies the concept of red teaming as a means by which to explore alternative strategies. We show how the model supports computer-based red teaming in several applications: realtime strategy games and critical infrastructure protection, using an evolutionary algorithm to automatically detect good and often surprising strategies.

A New Design For A Turing Test For Bots, Philip Hingston

Research outputs pre 2011

Interesting, human-like opponents add to the entertainment value of a video game, and creating such opponents is a difficult challenge for programmers. Can artificial intelligence and computational intelligence provide the means to convincingly simulate a human opponent? Or are simple programming tricks and deceptions more effective? To answer these questions, the author designed and organised a game bot programming competition, the BotPrize, in which competitors submit bots that try to pass a “Turing Test for Bots”. In this paper, we describe a new design for the competition, which will make it simpler to run, and, we hope, open up new …

Concept Learning By Example Decomposition, Sameer Joshi

Electronic Theses and Dissertations

For efficient understanding and prediction in natural systems, even in artificially closed ones, we usually need to consider a number of factors that may combine in simple or complex ways. Additionally, many modern scientific disciplines face increasingly large datasets from which to extract knowledge (for example, genomics). Thus to learn all but the most trivial regularities in the natural world, we rely on different ways of simplifying the learning problem. One simplifying technique that is highly pervasive in nature is to break down a large learning problem into smaller ones; to learn the smaller, more manageable problems; and then to …

Real-Time Automatic Price Prediction For Ebay Online Trading, Ilya Igorevitch Raykhel

Theses and Dissertations

While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; …

Simulation And Visualization Of Environments With Multidimensional Time, Luther A. Tychonievich

Theses and Dissertations

This work introduces the notion of computational hypertime, or the simulation and visualization of hypothetical environments possessing multidimensional time. An overview of hypertime is provided,including an intuitive visualization paradigm and a discussion of the failure of common simulation techniques when extended to include multidimensional time. A condition for differential equations describing hypertime motion to be amenable to standard time-iterative simulation techniques is provided,but is not satisfied by any known model of physics. An alternate simulation algorithm involving iterative refinement of entire equations of motion is presented,with an example implementation to solve elastic collisions in hypertime. An artificial intelligence algorithm for …

Learning Policies For Embodied Virtual Agents Through Demonstration, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura

Faculty Publications

Although many powerful AI and machine learning techniques exist, it remains difficult to quickly create AI for embodied virtual agents that produces visually lifelike behavior. This is important for applications (e.g., games, simulators, interactive displays) where an agent must behave in a manner that appears human-like. We present a novel technique for learning reactive policies that mimic demonstrated human behavior. The user demonstrates the desired behavior by dictating the agent’s actions during an interactive animation. Later, when the agent is to behave autonomously, the recorded data is generalized to form a continuous state-to-action mapping. Combined with an appropriate animation algorithm …

A Unified Framework For Solving Multiagent Task Assignment Problems, Kevin Cousin

Theses and Dissertations

Multiagent task assignment problem descriptors do not fully represent the complex interactions in a multiagent domain, and algorithmic solutions vary widely depending on how the domain is represented. This issue is compounded as related research fields contain descriptors that similarly describe multiagent task assignment problems, including complex domain interactions, but generally do not provide the mechanisms needed to solve the multiagent aspect of task assignment. This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains. Ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation are combined to …

Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas

Theses and Dissertations

Ant colony optimization algorithms have long been touted as providing an effective and efficient means of generating high quality solutions to NP-hard optimization problems. Unfortunately, while the structure of the algorithm is easy to parallelize, the nature and amount of communication required for parallel execution has meant that parallel implementations developed suffer from decreased solution quality, slower runtime performance, or both. This thesis explores a new strategy for ant colony parallelization that involves Area of Expertise (AOE) learning. The AOE concept is based on the idea that individual agents tend to gain knowledge of different areas of the search space …

Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries

Theses and Dissertations

This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich's MAXQ-Q HRL algorithm, the second a hierarchical ACO 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 and SARSA, with the modified ant colony optimization method, Ant-Q. …

A Classifier To Evaluate Language Specificity In Medical Documents, Trudi Miller '08, Gondy A. Leroy, Samir Chatterjee, Jie Fan, Brian Thoms '09

CGU Faculty Publications and Research

Consumer health information written by health care professionals is often inaccessible to the consumers it is written for. Traditional readability formulas examine syntactic features like sentence length and number of syllables, ignoring the target audience's grasp of the words themselves. The use of specialized vocabulary disrupts the understanding of patients with low reading skills, causing a decrease in comprehension. A naive Bayes classifier for three levels of increasing medical terminology specificity (consumer/patient, novice health learner, medical professional) was created with a lexicon generated from a representative medical corpus. Ninety-six percent accuracy in classification was attained. The classifier was then applied …

Using Machine Learning Techniques To Create Ai Controlled Players For Video Games, Bhuman Soni

Theses : Honours

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative …

Repurposing A Roomba: Evaluating And Training Behavior In A Simple Agent, Donald Samuel Abbott-Mccune

Theses and Dissertations

Recent attempts to reprogram a Roomba to be used as a simple agent have led to interesting behavior. Observation has shown that the behavior of the Roomba is not only dependent on the precepts of the Roomba, but also relies heavily on the uncontrollable environmental conditions that the Roomba is placed in. Ultimately this makes the Roomba a great platform to test and teach aspects of artificial intelligence. This paper will show how most of the tested environmental conditions are mitigated by a learning agent that will adjust behavior dependent on the precepts that are received.

Observational Intelligence: An Overview Of Computational Actual Entities And Their Use As Agents Of Artificial Intelligence, Brandon Scot Saunders

Theses and Dissertations

This thesis' focus is on the use of Alfred North Whitehead's concept of Actual Entities as a computational tool for computer science and the introduction of a novel usage of Actual Entities as learning agents. Actual Entities are vector based agents that interact within their environment through a process called prehension. It is the combined effect of multiple Actual Entities working within a Colony of Prehending Entities that produces emergent, intelligent behavior. It is not always the case that prehension functions for desired behavior are known beforehand and frequently the functions are too complex to construct by hand. Through the …

Event-Driven Document Selection For Terrorism, Zhen Sun, Ee Peng Lim, Kuiyu Chang, Teng-Kwee Ong, Rohan Kumar Gunaratna

Research Collection School Of Computing and Information Systems

In this paper, we examine the task of extracting information about terrorism related events hidden in a large document collection. The task assumes that a terrorism related event can be described by a set of entity and relation instances. To reduce the amount of time and efforts in extracting these event related instances, one should ideally perform the task on the relevant documents only. We have therefore proposed some document selection strategies based on information extraction (IE) patterns. Each strategy attempts to select one document at a time such that the gain of event related instance information is maximized. Our …

An Evolutionary Algorithm To Generate Ellipsoid Detectors For Negative Selection, Joseph M. Shapiro

Theses and Dissertations

Negative selection is a process from the biological immune system that can be applied to two-class (self and nonself) classification problems. Negative selection uses only one class (self) for training, which results in detectors for the other class (nonself). This paradigm is especially useful for problems in which only one class is available for training, such as network intrusion detection. Previous work has investigated hyper-rectangles and hyper-spheres as geometric detectors. This work proposes ellipsoids as geometric detectors. First, the author establishes a mathematical model for ellipsoids. He develops an algorithm to generate ellipsoids by training on only one class of …

Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN) -based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback …

The Evolution Of Intelligent Computer Software And The Semantic Web, Jens G. Pohl

Collaborative Agent Design (CAD) Research Center

The purpose of this paper is to trace the evolution of intelligent software from data-centric applications that essentially encapsulate their data environment to ontology-based applications with automated reasoning capabilities. The author draws a distinction between human intelligence and component capabilities within a more general definition of intelligence, which may be embedded in computer software. The primary vehicle in the quest for intelligent software has been the gradual recognition of the central role played by data and information, rather than the logic and functionality of the application. The three milestones in this evolution have been: the separation of data management from …

Computer-Assisted Music Composition In The 32-Bar Jazz Standard Form, Chad Hogg

Computer Science Honors Papers

The goal of this research project was to explore the potential for teaching a computer to compose jazz music. Specifically, the hope was to develop a method in which every decision could be made based on information about the entire system. If this problem were solved, the ancillary goal of allowing the user to set any arbitrary constraints that have global effects would be a trivial addition.

Intelligent Query Answering Through Rule Learning And Generalization, James M. Carsten

Theses and Dissertations

The Department of Defense (DoD) relies heavily on information systems to complete a myriad of tasks, from day-to-day personnel actions to mission critical imagery retrieval, intelligence analysis, and mission planning. The astronomical growth in size and performance of data storage systems leads to problems in processing the amount of data returned on any given query. Typical relational database systems return a set of unordered records. This approach is acceptable in small information systems, but in large systems, such as military image retrieval systems with more than 1 million records, it requires considerable time (often hours to days) to sort through …