Physical Sciences and Mathematics Commons^™

Reasoning Across Language And Vision In Machines And Humans, Andrei Barbu

Open Access Dissertations

Humans not only outperform AI and computer-vision systems, but use an unknown computational mechanism to perform tasks for which no suitable approaches exist. I present work investigating both novel tasks and how humans approach them in the context of computer vision and linguistics. I demonstrate a system which, like children, acquires high-level linguistic knowledge about the world. Robots learn to play physically-instantiated board games and use that knowledge to engage in physical play. To further integrate language and vision I develop an approach which produces rich sentential descriptions of events depicted in videos. I then show how to simultaneously detect …

Computational Intelligence And Decision Making: A Multidisciplinary Review, Renato Martins Alas, Sukanto Bhattacharya, Kuldeep Kumar

Kuldeep Kumar

The phenomenon of dynamic shift in our society called “speed up” has been part of the modern society since the middle of the eighteenth century. Its progressive development is already and will demand more speed in information processing. To cope with such fast pace demand of processing it is necessary to develop more sophisticated computational representation of the human brain. Computational Cognitive Neuroscience is the only realistic approach in reproducing the fundamental nature of human brain’s neurology. We support the biological computational representation of the human brain, based on fMRI imaging analysis, as more effective in the process of decision …

Recognition And Resolution Of 'Comprehension Uncertainty' In Ai, Sukanto Bhattacharya, Kuldeep Kumar

Kuldeep Kumar

Handling uncertainty is an important component of most intelligent behaviour – so uncertainty resolution is a key step in the design of an artificially intelligent decision system (Clark, 1990). Like other aspects of intelligent systems design, the aspect of uncertainty resolution is also typically sought to be handled by emulating natural intelligence (Halpern, 2003; Ball and Christensen, 2009). In this regard, a number of computational uncertainty resolution approaches have been proposed and tested by Artificial Intelligence (AI) researchers over the past several decades since birth of Al as a scientific discipline in early 1950s post- publication of Alan Turing's landmark …

Computer Sketch Recognition, Richard Steigerwald

Master's Theses

Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy.

In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced dataset …

Training An Asymmetric Signal Perceptron In An Artificial Chemistry, Peter Banda

Student Research Symposium

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability, and minimizes the system design to simple input-output specification. In this poster, I present a simulated chemical system, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry, which can successfully learn all 14 linearly separable logic functions. A perceptron is the simplest system capable of learning inspired by the functioning of a biological neuron. My newest model called the asymmetric signal perceptron (ASP) is, as opposed to its predecessors such as the weight-race perceptron (WRP), substantially simpler by …

Analysis Of Uncertain Data: Evaluation Of Given Hypotheses, Anatole Gershman, Eugene Fink, Bin Fu, Jaime G. Carbonell

Jaime G. Carbonell

We consider the problem of heuristic evaluation of given hypotheses based on limited observations, in situations when available data are insufficient for rigorous statistical analysis.

Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem, Jeremy Mange

Dissertations

The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either “friendly" or “enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next “timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with …

Artificial Intelligence And Data Mining: Algorithms And Applications, Jianhong Xia, Fuding Xie, Yong Zhang, Craig Caulfield

Research outputs 2013

Artificial intelligence and data mining techniques have been used in many domains to solve classification, segmentation, association, diagnosis, and prediction problems. The overall aim of this special issue is to open a discussion among researchers actively working on algorithms and applications. The issue covers a wide variety of problems for computational intelligence, machine learning, time series analysis, remote sensing image mining, and pattern recognition. After a rigorous peer review process, 20 papers have been selected from 38 submissions. The accepted papers in this issue addressed the following topics: (i) advanced artificial intelligence and data mining techniques; (ii) computational intelligence in …

Mobile Games With Intelligence: A Killer Application?, Philip Hingston, Clare Bates Congdon, Graham Kendall

Research outputs 2013

Mobile gaming is an arena full of innovation, with developers exploring new kinds of games, with new kinds of interaction between the mobile device, players, and the connected world that they live in and move through. The mobile gaming world is a perfect playground for AI and CI, generating a maelstrom of data for games that use adaptation, learning and smart content creation. In this paper, we explore this potential killer application for mobile intelligence. We propose combining small, light-weight AI/CI libraries with AI/CI services in the cloud for the heavy lifting. To make our ideas more concrete, we describe …

Testing A Distributed Denial Of Service Defence Mechanism Using Red Teaming, Samaneh Rastegari, Philip Hingston, Chiou-Peng Lam, Murray Brand

Research outputs 2013

The increased number of security threats against the Internet has made communications more vulnerable to attacks. Despite much research and improvement in network security, the number of denial of service (DoS) attacks has rapidly grown in frequency, severity, and sophistication in recent years. Thus, serious attention needs to be paid to network security. However, to create a secure network that can stay ahead of all threats, detection and response features are real challenges. In this paper, we look at the the interaction between the attacker and the defender in a Red Team/Blue Team exercise. We also propose a quantitative decision …

Interpreting Individual Classifications Of Hierarchical Networks, Will Landecker, Michael David Thomure, Luis M.A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, Steven P. Brumby

Computer Science Faculty Publications and Presentations

Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known …

Cancer Risk Analysis By Fuzzy Logic Approach And Performance Status Of The Model, Atinç Yilmaz, Kürşat Ayan

Turkish Journal of Electrical Engineering and Computer Sciences

Cancer is the leading life-threatening disease for people in today's world. Although cancer formation is different for each type of cancer, it has been determined by studies and research that stress also triggers cancer types. Early precaution is very important for people who have not fallen ill yet with a disease like cancer that has a high mortality rate and expensive treatment. With this study, we expound that the possibility of developing such disease may be decreased and people could take measures against it. For the 3 cancer types selected as pilot work by introducing a fuzzy logic model, the …

A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing …

Math, Minds, Machines, Christopher V. Carlile

Chancellor’s Honors Program Projects

No abstract provided.

Generalizing Agent Plans And Behaviors With Automated Staged Observation In The Real-Time Strategy Game Starcraft, Zackary A. Gill

Theses and Dissertations - UTB/UTPA

In this thesis we investigate the processes involved in learning to play a game. It was inspired by two observations about how human players learn to play. First, learning the domain is intertwined with goal pursuit. Second, games are designed to ramp up in complexity, walking players through a gradual cycle of acquiring, refining, and generalizing knowledge about the domain. This approach does not rely on traces of expert play. We created an integrated planning, learning and execution system that uses StarCraft as its domain. The planning module creates command/event groupings based on the data received. Observations of unit behavior …

Multiview Semi-Supervised Learning With Consensus, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones. This paper demonstrates a way to improve the transductive SVM, which is an existing semi-supervised learning algorithm, by employing a multiview learning paradigm. Multiview learning is based on the fact that for some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such …

Parsing Combinatory Categorial Grammar Via Planning In Answer Set Programming, Yuliya Lierler, Peter Schueller

Computer Science Faculty Books and Monographs

Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, from Correct reasoning: essays on logic-based AI in honour of Vladimir Lifschitz, co-authored by Yuliya Lierler, UNO faculty member. Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) - a declarative programming paradigm. We formulate the …

Correct Reasoning: Essays On Logic-Based Ai In Honour Of Vladimir Lifschitz, Esta Erdem, Joohyung Lee, Yuliya Lierler, David Pearce

Faculty Books and Monographs

Co-edited by Yuliya Lierler, UNO faculty member.

Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, co-authored by Yuliya Lierler, UNO faculty member.

This Festschrift published in honor of Vladimir Lifschitz on the occasion of his 65th birthday presents 39 articles by colleagues from all over the world with whom Vladimir Lifschitz had cooperation in various respects. The 39 contributions reflect the breadth and the depth of the work of Vladimir Lifschitz in logic programming, circumscription, default logic, action theory, causal reasoning and answer set programming.

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.

Comparing Ai Archetypes And Hybrids Using Blackjack, Robert Edward Noonan

All Graduate Theses, Dissertations, and Other Capstone Projects

The discipline of artificial intelligence (AI) is a diverse field, with a vast variety of philosophies and implementations to consider. This work attempts to compare several of these paradigms as well as their variations and hybrids, using the card game of blackjack as the field of competition. This is done with an automated blackjack emulator, written in Java, which accepts computer-controlled players of various AI philosophies and their variants, training them and finally pitting them against each other in a series of tournaments with customizable rule sets. In order to avoid bias towards any particular implementation, the system treats each …

Parsing Combinatory Categorial Grammar Via Planning In Answer Set Programming, Yuliya Lierler, Peter Schueller

Yuliya Lierler

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 …

Single And Multiobjective Approaches To Clustering With Point Symmetry., Sriparna Saha Dr.

Doctoral Theses

In our every day life, we make decisions consciously or unconsciously. This decision can be very simple such as selecting the color of dress or deciding the menu for lunch, or may be as difficult as those involved in designing a missile or in selecting a career. The former decision is easy to take, while the latter one might take several years due to the level of complexity involved in it. The main goal of most kinds of decision-making is to optimize one or more criteria in order to achieve the desired result. In other words, problems related to optimization …

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 …

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 …

Computer-Guided Solutions To Physics Problems Using Prolog, Thomas J. Bensky, Catherine A. Taff

Physics

By posing a continual stream of pertinent questions, a nonmathematical computer program can prod freshman physics students toward an analytical solution to one-dimensional kinematics problems.

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.