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  Partner: UNT Libraries
 Degree Discipline: Computer Science
 Degree Level: Doctoral
A Mechanism for Facilitating Temporal Reasoning in Discrete Event Simulation

A Mechanism for Facilitating Temporal Reasoning in Discrete Event Simulation

Date: May 1992
Creator: Legge, Gaynor W.
Description: This research establishes the feasibility and potential utility of a software mechanism which employs artificial intelligence techniques to enhance the capabilities of standard discrete event simulators. As background, current methods of integrating artificial intelligence with simulation and relevant research are briefly reviewed.
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Using Extended Logic Programs to Formalize Commonsense Reasoning

Using Extended Logic Programs to Formalize Commonsense Reasoning

Date: May 1992
Creator: Horng, Wen-Bing
Description: In this dissertation, we investigate how commonsense reasoning can be formalized by using extended logic programs. In this investigation, we first use extended logic programs to formalize inheritance hierarchies with exceptions by adopting McCarthy's simple abnormality formalism to express uncertain knowledge. In our representation, not only credulous reasoning can be performed but also the ambiguity-blocking inheritance and the ambiguity-propagating inheritance in skeptical reasoning are simulated. In response to the anomalous extension problem, we explore and discover that the intuition underlying commonsense reasoning is a kind of forward reasoning. The unidirectional nature of this reasoning is applied by many reformulations of the Yale shooting problem to exclude the undesired conclusion. We then identify defeasible conclusions in our representation based on the syntax of extended logic programs. A similar idea is also applied to other formalizations of commonsense reasoning to achieve such a purpose.
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Using Normal Deduction Graphs in Common Sense Reasoning

Using Normal Deduction Graphs in Common Sense Reasoning

Date: May 1992
Creator: Munoz, Ricardo A. (Ricardo Alberto)
Description: This investigation proposes a powerful formalization of common sense knowledge based on function-free normal deduction graphs (NDGs) which form a powerful tool for deriving Horn and non-Horn clauses without functions. Such formalization allows common sense reasoning since it has the ability to handle not only negative but also incomplete information.
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Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design

Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design

Date: December 1993
Creator: Halverson, Ranette Hudson
Description: The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching.
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Multiresolutional/Fractal Compression of Still and Moving Pictures

Multiresolutional/Fractal Compression of Still and Moving Pictures

Date: December 1993
Creator: Kiselyov, Oleg E.
Description: The scope of the present dissertation is a deep lossy compression of still and moving grayscale pictures while maintaining their fidelity, with a specific goal of creating a working prototype of a software system for use in low bandwidth transmission of still satellite imagery and weather briefings with the best preservation of features considered important by the end user.
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Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm

Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm

Date: December 1993
Creator: Civelek, Ferda N. (Ferda Nur)
Description: Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, has been introduced. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications. A temporal backpropagation algorithm which supports the model has been developed. The model along with the temporal backpropagation algorithm makes it extremely practical to define any artificial neural network application. Also, an approach that can be followed to decrease the memory space used by weight matrix has been introduced. The algorithm was tested using a medical connectionist expert system to show how best we describe not only the disease but also the entire course of the disease. ...
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A Multi-Time Scale Learning Mechanism for Neuromimic Processing

A Multi-Time Scale Learning Mechanism for Neuromimic Processing

Date: August 1994
Creator: Mobus, George E. (George Edward)
Description: Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these problems. The adaptrode is shown to emulate the dynamics of real biological synapses and represents a significant departure from the classical weighted input scheme of conventional artificial neural networks. Indeed the adaptrode is shown, by analysis of the deep structure of real synapses, to have a strong structural correspondence with the latter in terms of multi-time scale biophysical processes. Simulations of an adaptrode-based neuron and a small network of neurons are shown to have the same learning capabilities as invertebrate animals in classical conditioning. Classical conditioning is considered a fundamental learning task in animals. Furthermore, it is subject to temporal ordering constraints that fulfill the criteria of causal relations in natural systems. It may offer clues to the learning of causal relations and mechanisms for causal reasoning. The ...
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A Highly Fault-Tolerant Distributed Database System with Replicated Data

A Highly Fault-Tolerant Distributed Database System with Replicated Data

Date: December 1994
Creator: Lin, Tsai S. (Tsai Shooumeei)
Description: Because of the high cost and impracticality of a high connectivity network, most recent research in transaction processing has focused on a distributed replicated database system. In such a system, multiple copies of a data item are created and stored at several sites in the network, so that the system is able to tolerate more crash and communication failures and attain higher data availability. However, the multiple copies also introduce a global inconsistency problem, especially in a partitioned network. In this dissertation a tree quorum algorithm is proposed to solve this problem, imposing a logical tree structure along with dynamic system reconfiguration on all the copies of each data item. The proposed algorithm can be viewed as a dynamic voting technique which, with the help of an appropriate concurrency control algorithm, exhibits the major advantages of quorum-based replica control algorithms and of the available copies algorithm, so that a single copy is read for a read operation and a quorum of copies is written for a write operation. In addition, read and write quorums are computed dynamically and independently. As a result expensive read operations, like those that require several copies of a data item to be read in most ...
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Recognition of Face Images

Recognition of Face Images

Date: December 1994
Creator: Pershits, Edward
Description: The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: ...
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A Theoretical Network Model and the Incremental Hypercube-Based Networks

A Theoretical Network Model and the Incremental Hypercube-Based Networks

Date: May 1995
Creator: Mao, Ai-sheng
Description: The study of multicomputer interconnection networks is an important area of research in parallel processing. We introduce vertex-symmetric Hamming-group graphs as a model to design a wide variety of network topologies including the hypercube network.
Contributing Partner: UNT Libraries
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