Digital Commons Network^™

Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad

Theses and Dissertations

This research presents an autonomous and computationally tractable method for scientific process analysis, combining an iterative algorithmic search and a recognition technique to discover multivariate linear and non-linear relations within experimental data series. These resultant data-driven relations provide researchers with a potentially real-time insight into experimental process phenomena and behavior. This method enables the efficient search of a potentially infinite space of relations within large data series to identify relations that accurately represent process phenomena. Proposed is a time series transformation that encodes and compresses real-valued data into a well-defined, discrete-space of 13 primitive elements where comparative evaluation between variables …

Utilizing Data And Knowledge Mining For Probabilistic Knowledge Bases, Daniel J. Stein Iii

Theses and Dissertations

Problems can arise whenever inferencing is attempted on a knowledge base that is incomplete. Our work shows that data mining techniques can be applied to fill in incomplete areas in Bayesian Knowledge Bases (BKBs), as well as in other knowledge-based systems utilizing probabilistic representations. The problem of inconsistency in BKBs has been addressed in previous work, where reinforcement learning techniques from neural networks were applied. However, the issue of automatically solving incompleteness in BKBs has yet to be addressed. Presently, incompleteness in BKBs is repaired through the application of traditional knowledge acquisition techniques. We show how association rules can be …

On The Impact Of Forgetting On Learning Machines, Rūsiņš Freivalds, Efim Kinber, Carl H. Smith

School of Computer Science & Engineering Faculty Publications

People tend not to have perfect memories when it comes to learning, or to anything else for that matter. Most formal studies of learning, however, assume a perfect memory. Some approaches have restricted the number of items that could be retained. We introduce a complexity theoretic accounting of memory utilization by learning machines. In our new model, memory is measured in bits as a function of the size of the input. There is a hierarchy of learnability based on increasing memory allotment. The lower bound results are proved using an unusual combination of pumping and mutual recursion theorem arguments. For …

Intelligent Control Of Vehicles: Preliminary Results On The Application Of Learning Automata Techniques To Automated Highway System, Cem Unsal, John S. Bay, Pushkin Kachroo

Electrical & Computer Engineering Faculty Research

We suggest an intelligent controller for an automated vehicle to plan its own trajectory based on sensor and communication data received. Our intelligent controller is based on an artificial intelligence technique called learning stochastic automata. The automaton can learn the best possible action to avoid collisions using the data received from on-board sensors. The system has the advantage of being able to work in unmodeled stochastic environments. Simulations for the lateral control of a vehicle using this AI method provides encouraging results.

A Fortran Based Learning System Using Multilayer Back-Propagation Neural Network Techniques, Gregory L. Reinhart

Theses and Dissertations

An interactive computer system which allows the researcher to build an optimal neural network structure quickly, is developed and validated. This system assumes a single hidden layer perceptron structure and uses the back- propagation training technique. The software enables the researcher to quickly define a neural network structure, train the neural network, interrupt training at any point to analyze the status of the current network, re-start training at the interrupted point if desired, and analyze the final network using two- dimensional graphs, three-dimensional graphs, confusion matrices and saliency metrics. A technique for training, testing, and validating various network structures and …

Discovery Learning In Autonomous Agents Using Genetic Algorithms, Edward O. Gordon

Theses and Dissertations

As the new Distributed Interactive Simulation (DIS) draft standard evolves into a useful document and distributed simulations begin to emerge that implement parts of the standard, there is renewed interest in available methods to effectively control autonomous aircraft agents in such a simulated environment. This investigation examines the use of a genetics-based classifier system for agent control. These are robust learning systems that use the adaptive search mechanisms of genetic algorithms to guide the learning system in forming new concepts (decision rules) about its environment. By allowing the rule base to evolve, it adapts agent behavior to environmental changes. Addressed …

Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera

Theses and Dissertations

Computer-generated autonomous agents in simulation often behave predictably and unrealistically. These characteristics make them easy to spot and exploit by human participants in the simulation, when we would prefer the behavior of the agent to be indistinguishable from human behavior. An improvement in behavior might be possible by enlarging the library of responses, giving the agent a richer assortment of tactics to employ during a combat scenario. Machine learning offers an exciting alternative to constructing additional responses by hand by instead allowing the system to improve its own performance with experience. This thesis presents NOSTRUM, a discovery-based learning DBL system …

Learning In Connectionist Networks Using The Alopex Algorithm, Kootala Pattath Venugopal

Electronic Theses and Dissertations 1990 - 1999

The Alopex algorithm is presented as a universal learning algorithm for connectionist models. It is shown that the Alopex procedure could be used efficiently as a supervised learning algorithm for such models. The algorithm is demonstrated successfully on a variety of network architectures. Such architectures include multilayer perceptrons, time-delay models, asymmetric, fully recurrent networks and memory neuron networks. The learning performance as well as the generation capability of the Alopex algorithm are compared with those of the backpropagation procedure, concerning a number of benchmark problems, and it is shown that the Alopex has specific advantages over the backpropagation. Two new …

Multiple Learner Systems Using Resampling Methods, Binyun Xie

Computer Science Theses & Dissertations

The N-Learners Problem deals with combining a number of learners such that the resultant system is "better", under some criterion, than the best of the individual learners. We consider a system of probably approximately correct concept learners. Depending on the available information, there are several methods to make the composite system better than the best of the individual learners. If a sample and an oracle that generates data points (but, not their classification) is available, then we show that we can achieve arbitrary levels of the normalized confidence of the composite system if (a) a robust learning algorithm is available, …

An Examination And Analysis Of The Boltzmann Machine, Its Mean Field Theory Approximation, And Learning Algorithm, Vincent Clive Phillips

Theses : Honours

It is currently believed that artificial neural network models may form the basis for inte1ligent computational devices. The Boltzmann Machine belongs to the class of recursive artificial neural networks and uses a supervised learning algorithm to learn the mapping between input vectors and desired outputs. This study examines the parameters that influence the performance of the Boltzmann Machine learning algorithm. Improving the performance of the algorithm through the use of a naïve mean field theory approximation is also examined. The study was initiated to examine the hypothesis that the Boltzmann Machine learning algorithm, when used with the mean field approximation, …

Learning Algorithms For Artificial Neural Networks: A Closer Look At Back Propagation, Simplex And Monte Carlo, Sergio Alberto Cedeño

Graduate Theses and Dissertations

No abstract provided.

Learning System, Bhavesh T. Adani

Theses

Many rule-based learning systems have been implemented which generate and update rules/facts from illustrative examples provided by the user. But very few of them have succeeded in achieving the efficiency and accuracy of an ideal system.

The approach made here is to continuously update the hierarchical structure of rules/facts of a knowledge-base so that efficient and accurate solutions to any query can be achieved easily and immediately from the many possible solutions to the query. As the rule-based systems deal with a very large knowledge-base, it is sometimes necessary to apply a learning technique that finds optimum solutions efficiently.

The …

Schematic: An Experiment In Machine Learning Using Conceptual Graphs, Richard L. Halterman

Electronic Theses and Dissertations 1980 - 1989

Conceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base, including the concept type hierarchy, the relation list, canonical forms, and the current domain, are dynamically maintained. Teacher interaction is handled exclusively with conceptual …

Artificial Intelligence: Myths And Realities, Hugo D'Alarcao

Bridgewater Review

Artificial intelligence the name conjures images of mechanical monsters, the Golem, Dr. Frankenstein’s creation and the rebellious computer Hal. We have always been fascinated by the possibility of creating a machine in our image, but this fascination is often accompanied by apprehension. We fear losing control of our creation and suspect that it might turn against us. It is this duality, this conflict between the desire to create and the fear of the consequences of the creation that has been so successfully exploited by writers. It is also, in part, this fascination that has recently brought the field of Artificial …

A Design Methodology For Automated Inspection, Donald Henrik Kylin

Research Reports from the Department of Operations

A class of industrial inspection problems, important because it often causes production bottlenecks, is posed as a pattern recognition problem. A typical member of this class is the inspection of surface quality in flat-rolled steel. Varying inspection requirements, the limitations of manual visual inspection, and the production potential of modern plants motivate research into automatic inspection. Operations research methodologies are applied to three outstanding problems in applied pattern recognition in the context of the automatic inspection system design problem. The design methodology for the inspection system, as proposed in block diagram form, is based on the solutions to these three …