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Acquiring symbolic design optimization problem reformulation knowledgeSarkar, Somwrita. January 2009 (has links)
Thesis (Ph. D.)--University of Sydney, 2009. / Title from title screen (viewed November 13, 2009). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Faculty of Architecture, Design and Planning in the Faculty of Science. Includes graphs and tables. Includes bibliographical references. Also available in print form.
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Knowledge representation and open world planning using [Greek letter Psi]-forms /Babaian, Tamara. January 2000 (has links)
Thesis (Ph.D )--Tufts University, 2000. / Adviser: James G. Schmolze. Submitted to the Dept. of Computer Science. Includes bibliographical references (leaves 148-156). Access restricted to members of the Tufts University community. Also available via the World Wide Web;
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Knowledge retention with genetic algorithms by multiple levels of representation /Ding, Yingjia, January 1991 (has links)
Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 93-94). Also available via the Internet.
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Holographic reduced representation : distributed representation for cognitive structures /Plate, Tony A., January 2003 (has links)
Teilw. zugl.: @Toronto, Univ., Diss., 1994.
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A formal model for fuzzy ontologies.January 2006 (has links)
Au Yeung Ching Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 97-110). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Semantic Web and Ontologies --- p.3 / Chapter 1.2 --- Motivations --- p.5 / Chapter 1.2.1 --- Fuzziness of Concepts --- p.6 / Chapter 1.2.2 --- Typicality of Objects --- p.6 / Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8 / Chapter 1.3 --- Objectives --- p.9 / Chapter 1.4 --- Contributions --- p.10 / Chapter 1.5 --- Structure of the Thesis --- p.11 / Chapter 2 --- Background Study --- p.13 / Chapter 2.1 --- The Semantic Web --- p.14 / Chapter 2.2 --- Ontologies --- p.16 / Chapter 2.3 --- Description Logics --- p.20 / Chapter 2.4 --- Fuzzy Set Theory --- p.23 / Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25 / Chapter 2.5.1 --- Theory of Concepts --- p.26 / Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28 / Chapter 2.5.3 --- Similarity between Concepts --- p.29 / Chapter 2.5.4 --- Context and Context Effects --- p.31 / Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33 / Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35 / Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39 / Chapter 2.9 --- Contextual Reasoning --- p.41 / Chapter 3 --- A Formal Model of Ontology --- p.44 / Chapter 3.1 --- Rationale --- p.45 / Chapter 3.2 --- Concepts --- p.47 / Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47 / Chapter 3.4 --- Subsumption of Concepts --- p.49 / Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51 / Chapter 3.6 --- Prototype Vector and Typicality --- p.54 / Chapter 3.7 --- An Example --- p.59 / Chapter 3.8 --- Similarity between Concepts --- p.61 / Chapter 3.9 --- Context and Contextualization of Ontology --- p.65 / Chapter 3.9.1 --- Formal Definitions --- p.67 / Chapter 3.9.2 --- Contextualization of an Ontology --- p.69 / Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71 / Chapter 4 --- Discussions and Analysis --- p.73 / Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73 / Chapter 4.2 --- Likeliness and Typicality --- p.78 / Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81 / Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81 / Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82 / Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83 / Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84 / Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85 / Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86 / Chapter 4.5 --- Potential Application of the Model --- p.88 / Chapter 4.5.1 --- Searching in the Semantic Web --- p.88 / Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90 / Chapter 5 --- Conclusions and Future Work --- p.91 / Chapter 5.1 --- Conclusions --- p.91 / Chapter 5.2 --- Future Research Directions --- p.93 / Publications --- p.96 / Bibliography --- p.97
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Structure of Knowledge Representation and ReasoningJanuary 1988 (has links)
This thesis deals with one important aspect of Artificial Intelligence,
knowledge representation and reasoning. Problems like knowledge representation
hypothesis, production systems, and the proceduralist and declarativist controversy
are discussed. The conventional concept of Al system is based on a
model where the reasoning and knowledge base are separate. A different model
is proposed in this thesis. In this model, the sensory knowledge is considered the
basic elements of the knowledge base, and the higher level knowledge form a
hierarchical structure. Reasoning power, in this model, is only part of the,
knowledge structure.
A frame-based knowledge representation system, WenLy, is presented. It is
designed to construct a knowledge base from the bottom up, which is conformed
to the proposed model in this thesis. The unique characteristic of WenLy is that
its reasoning is accomplished by the code imbedded in the knowledge base
instead of a separate inference engine. This is the correct approach according to
the knowledge structure model herein proposed.
An example of the application is shown. WenLy can be used by a robot to
represent the robot's understanding of the outside world. The understanding,
however, is limited by the bandwidth of the perception capabilities. It can be
shown that the conflict between the declarative and procedural aspects of
knowledge can be resolved by following the proposed model.
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Revision programming a knowledge representation formalism /Pivkina, Inna Valentinovna, January 2001 (has links) (PDF)
Thesis (Ph. D.)--University of Kentucky, 2001. / Title from document title page. Document formatted into pages; contains vii, 121 p. : ill. Includes abstract. Includes bibliographical references (p. 116-119).
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Individual differences in knowledge representation and problem- solving performance in physicsAustin, Lydia B. (Lydia Bronwen) January 1992 (has links)
Concept mapping in college-level physics was investigated. The study was carried out in three parts. First, an attempt was made to validate concept mapping as a method of evaluating student learning at the junior college level (ages 16-21). Several measures were found to be sensitive to differences in students' achievement. Second, the effectiveness of concept mapping as an instructional strategy was investigated. It was found that the strategy led to improvement in multistep problem-solving performance but not in performance on single step problems. Third, the concept maps made by experts in the field were compared with the maps made by high achieving and average achieving students to see if this is yet another way in which high performance and expertise are related. It was found that the high achieving students made maps which more nearly resembled the maps made by experts than those made by average achieving students.
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Phases of knowledge in lexical acquisition : a developmental study into four to twelve year olds decipherment of unfamiliar words from linguistic contexts during continuous assessmentBjarnadottir, Bjorg January 1996 (has links)
Research on the deciphering of nonsense words within the context of text, a story, or tale was conducted at various schools and day-care centres in the Stirling area of Scotland in 1985-1988. Three experiments were conducted, in which large samples of primary school children aged 4-12 were tested. The experiments resembled Werner and Kaplan's (1950) "Word-Context Task, " in which isolated sentences in a series with one nonsense word in each sentence were presented to school children. The children were asked to answer questions about the meanings of these words. The results were not in line with the rapid word learning that experience suggests happens in young children, it was not until after age 9 that the children started to give approximately correct answers, and prior to age 11 the answers did not meet up with proper adult definitions. It has been pointed out, however (Donaldson, 1978), that because these sentences were not supported by any relation to immediate context and behaviour, and because the children were required to process utterances as pure isolated language - an unnatural situation for language acquisition - the "Word-Context Task" may have given an unrealistic picture of the child's ability to acquire language naturally. In the three word-leaming studies at Stirling University in 1985-1988, in order to account for a more natural presentation, the sentences with the nonsense word were embodied in the context of a story. Children were thought to fare better (than the children in the Wemer & Kaplan study) when listening to such a story, especially if the basic theme was of interest. A methodological tool, refined in the work of Dockrell (1981), in which the full meaning of a term involves having worked out the sense, reference, and denotation of the term. was applied in each of the test batteries that followed the presentation of the story. In these tests, the children were tested on both their comprehension and production of the new term in question. Drawings were used in order to try to tap the children's denotation of the new term, and to facilitate young children's approach to the demands of the study. As regards word meaning in general. Martin Joos (1972) had argued that the common blunder was that an odd word must have an odd sense--the odder, the better. He argued that one should define words in such a fashion as to make them contribute least to the total message derivable from its passage where it is housed, rather than, e. g., defining it according to some presumed etymology of semantic history. He called this concept "a tacit principle", and argued that word learners and word users would sense the intuitive familiarity of the conveyed meaning of words and text. Words are, according to this principle, "mysterious" in their environment, their meanings are not worked out deliberately, intentionally; rather, one should make the mysterious item maximally supportive and supported in its situation, in order that redundancy would result in proper connotation of the distributed meaning. Context and knowledge of contexts reveal meaning; the text is processed holistically, and so are the instantaneous meanings of the words of which it is composed. Thus, Joos maintained that in deciphering an unknown word, the wisest course is to assume the "least meaning" consistent with the context. Tasks such as Werner and Kaplan's "Word-Context Task" (1950), force subjects to infer aspects of meaning that go well beyond this "least" meaning and, as Joos pointed out, this leads notably to errors from which recovery is difficult. In the studies at Stirling University, attempts were made to determine if different types of learning would result in different types of responses. The dichotomy, intentional/incidental or analytic/holistic was worked out into experimental and control conditions, as based on Aveling's pioneering experiment (1911, 1912) into the general and particular aspects of encoded stimuli. Later, Lee Brooks (1978) worked with the dichotomies intentional/incidental in his Lepton experiments and argued that the more complex a behaviour is (speaking or writing, for example), the more likely it is to be learned implicitly. He pointed out, however, that the dichotomies explicit/implicit, analytic/non analytic, and deliberate vs. intuitive processes need to be elaborated and not taken as a strict division. In the three experiments at Stirling, children of primary school age (ages 4 to 12) were presented with a "word-context" task and their understanding of the unknown word was probed under different conditions. In the control condition a control word was probed, but in the experimental condition the child's understanding of the target word was fully tested. All the children listened to a short story displayed by a video or read from a tape in which the unknown word occurred in several different contexts, the unknown word in each story denoted an unfamiliar natural kind. During the story's display, children in the control condition were, at certain intervals, asked questions about the story's theme. Children in the experimental group were, at these same intervals, shown a sample of objects, to one of which the unknown word referred, and they were asked to hand these objects to the experimenter as she requested the objects, or they were asked direct questions about the meaning of the target word and about other words in the story. After hearing the story, all subjects were tested on their comprehension and production of the unknown word, together with other words, and a scoring procedure based on a technique developed by Dockrell (1981) was applied. This procedure necessitated the full meaning of the term covering aspects of the sense, reference., and denotation of the new term (cf. Lyons, 1977a). The results indicate that children younger than those tested in the Wemer and Kaplan's "Word-Context Task" (ages 8.6 to 13.6) could decipher the full meaning of the new term. But individual differences within age groups showed greater differences than existed between age groups. All in all, the results indicate that working out the full meaning of a new term is a lengthy process indeed (Campbell & Dockrell, 1986), even though a sense of the given semantic domain may often be established quite early in the learning process. Performance styles also differ from younger children to older ones. The results indicate that there were significant age differences between the children in the first and second experiments, but that such differences were lacking in the third experiment, and that control subjects in the three studies seldom gave poorer responses than did experimental subjects and often did better. However, the results must be interpreted in the light of learning and recovery from error occurring, within the experimental subjects in the course of deciphering. If the initial scores of the experimental subjects on the target word as obtained during encoding are compared with the first scores obtained from the control subjects after they had heard the whole story, there is a significant difference in scores between the conditions in favour of the control subjects in all age groups. This is consistent with Joos's assumption that an interference concerning the meaning of a word that occurs too early in the learning task and not enough information of contextual cues will lead the children in the experimental groups astray in their guesses when asked too early for answers on the new word's meaning. But implied in Joos's Axiom is the likelihood for recoveries from errors, and the strategies children use in order to work them out need to be explored further. Much individual variation was found among the children's responses in the age groups. These differences were indeed more significant than were the differences between age groups.
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Conceptual reasoning : belief, multiple agents and preference / by Krzysztof Zbigniew Nowak.Nowak, Krzysztof Zbigniew January 1998 (has links)
Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998
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