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

Thesis (Ph. D.)--University of North Texas, Aug., 2007. / Title from title page display. Includes bibliographical references.

Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998. / Bibliography: p. 121-125.

Thesis (Ph. D.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Oct. 20, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Electrical and Computer Engineering, University of Alberta." Includes bibliographical references.

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During the early modern period, practitioners in oftentimes unrelated arts and sciences began to experiment with transcribing and disseminating technical information by means of new symbolic notations. Algebra, music, chemistry, dance—whole fields of knowledge were quite literally rewritten with plus signs, treble clefs, affinity tables, and step symbols. “The Art of Signs” examines why early modern people working within and across disciplinary boundaries converged on the idea that developing complex symbolic notations would ultimately be worthwhile by reconstructing the reasons that they gave for doing so. It argues that symbolic notations appealed because they enabled powerful techniques of “visual thinking” that had no analogue in more conventional methods of inquiry. Notations transformed problems of information into problems of visualization whose solutions could then be derived by manipulating the properties of the drawn, two-dimensional plane. Indeed, early modern proponents of notations frequently described them in terms of vision, of being able to “see” things with them that they had not recognized before. However, because established methods of reasoning were predominantly verbal or empirical, symbolic notations and the visual thinking that they entailed necessarily challenged received ideas about how information ought to be represented and how knowledge ought to be discovered. Critics of the new notations argued that, at best, they amounted to a form of intellectual obscurantism that stymied rather than facilitated the circulation of knowledge. At worst, notations harbored disturbing implications for human ingenuity if the generation of new ideas truly could be reduced to the ranging and rearranging of symbols on a piece of paper. All told, “The Art of Signs” argues that early modern debates about the use and abuse of symbolic notations represent an underappreciated component of the epistemological ruptures that characterize the Scientific Revolution. Moreover, by recovering early modern understandings of symbolic notation, this dissertation demonstrates that a historical treatment of early modern semiotic thought can be leveraged to take a fresh look at perennial questions of representation that concern scholars across the humanities.

Thesis (Ph. D.)--University of Kentucky, 2001. / Title from document title page. Document formatted into pages; contains vii, 75 p. : ill. Includes abstract. Includes bibliographical references (p. 70-72).

Knowledge-based Cascade-correlation is a neural network algorithm that combines inductive learning and knowledge transfer (Shultz & Rivest, 2001). In the present thesis, this algorithm is tested on several real-world and artificial problems, and extended in several ways. The first extension consists in the incorporation of the Knowledge-based Artificial Neural Network (KBANN; Shavlik, 1994) technique for generating rule-based (RBCC) networks. The second extension consists of the adaptation of the Optimal Brain Damage (OBD; LeCun, Denker, & Solla, 1990) pruning technique to remove superfluous connection weights. Finally, the third extension consists in a new objective function based on information theory for controlling the distribution of knowledge attributed to subnetworks. A simulation of lexical ambiguity resolution is proposed. In this study, the use of RBCC networks is motivated from a cognitive and neurophysiological perspective.

Thesis (MSc(Comp & InfoSc))--University of South Australia, 2001

Thesis (M. Sc.)--York University, 2005. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 90-94). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004 & res_dat=xri:pqdiss & rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation & rft_dat=xri:pqdiss:MR11752.