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Full-Text Articles in Physical Sciences and Mathematics
Inference Algorithm Performance And Selection Under Constrained Resources, Brett J. Borghetti
Inference Algorithm Performance And Selection Under Constrained Resources, Brett J. Borghetti
Theses and Dissertations
Knowing that reasoning over probabilistic networks is, in general, NP-hard, and that most reasoning environments have limited resources, we need to select algorithms that can solve a given problem as fast as possible. This thesis presents a method for predicting the relative performance of reasoning algorithms based on the domain characteristics of the target knowledge structure. Armed with this knowledge, the research shows how to choose the best algorithm to solve the problem. The effects of incompleteness of the knowledge base at the time of inference is explored, and requirements for reasoning over incompleteness are defined. Two algorithms for reasoning …
Utilizing Data And Knowledge Mining For Probabilistic Knowledge Bases, Daniel J. Stein Iii
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 …
A Test-Case Based Approach To Bayesian Knowledge Base Incompleteness Detection And Correction, Louise J. Lyle
A Test-Case Based Approach To Bayesian Knowledge Base Incompleteness Detection And Correction, Louise J. Lyle
Theses and Dissertations
This work develops tools and techniques to identify particular Bayesian Knowledge Base (BKB) incompletenesses, and to modify the existing knowledge-base (KB) structure to correct these problems. The methodology performs manually or automatically, informing the user of either problems causing the incompleteness, or of details resulting from the automatic knowledge-base correction. The proposed methodology is designed for integration with BVAL, to augment BVAL's validation techniques.
Knowledge Discovering For Document Classification Using Tree Matching In Texpros, Ching-Song Wei
Knowledge Discovering For Document Classification Using Tree Matching In Texpros, Ching-Song Wei
Dissertations
This dissertation describes a knowledge-based system for classifying documents based upon the layout structure and conceptual information extracted from the content of the document. The spatial elements in a document are laid out in rectangular blocks which are represented by nodes in an ordered labelled tree, called the "layout structure tree" (L-S Tree). Each leaf node of a L-S Tree points to its corresponding block content. A knowledge Acquisition Tool (KAT) is devised to create a Document Sample Tree from L-S Tree, in which each of its leaves contains a node content conceptually describing its corresponding block content. Then, applying …
A Generic Intelligent Architecture For Computer-Aided Training Of Procedural Knowledge, Freeman A. Kilpatrick Jr.
A Generic Intelligent Architecture For Computer-Aided Training Of Procedural Knowledge, Freeman A. Kilpatrick Jr.
Theses and Dissertations
Intelligent Tutoring System (ITS) development is a knowledge-intensive task, suffering from the same knowledge acquisition bottleneck that plagues most Artificial Intelligence (AI) systems. This research presents an architecture that requires knowledge only in the form of a shallow knowledge base and a simulation to produce a training system. The knowledge base provides the basic procedural knowledge while the simulation provides context. The remainder of the knowledge required for training is learned through the interaction of these components in a state-space scenario exploration process and inductive machine learning. These knowledge components are used only at the interface level, allowing the internal …