Thesis (Ph. D.)--Stanford University, 1986. / "September 1986." "This work was partially supported by the Office of Naval Research under contracts number N00014-81-K-0303 and N00014-81-K-0004, by the National Institutes of Health under grant number 5P41 RR 00785, and by the Defense Advanced Research Projects Agency under contract number N00039-86-C-0033"--P. vi. Bibliography: p. 160-167.

The transition of emphasis in business competition from a technology-led age to a market-oriented era has led to a rapid shift from the conventional "economy of scale" towards the "economy of scope" in contemporary manufacturing. Hence, it is necessary and essential to be able to respond to the dynamic market and customer requirements systematically and consistently. The central theme of this research is to rationalise and improve the conventional means of analysing and interpreting the linguistic and often imprecise customer requirements in order to identify the essential product features and determine their appropriate design targets dynamically and quantitatively through a series of well proven methodologies and techniques. The major objectives of this research are: a) To put forward a hybrid approach for decoding and processing the Voice of Customer (VoC) in order to interpret the specific customer requirements and market demands into definitive product design features, and b) To quantify the essential product design features with the appropriate technical target values for facilitating the downstream planning and control activities in delivering the products or services. These objectives would be accomplished through activities as follows: • Investigating and understanding the fundamental nature and variability of customer attributes (requirements); • Surveying and evaluating the contemporary approaches in handling customer attributes; • Proposing an original and generic hybrid model for categorising, prioritising and interpreting specific customer attributes into the relevant product attributes with tangible target values; • Developing a software system to facilitate the implementation of the proposed model; • Demonstrating the functions of the hybrid model through a practical case study. This research programme begins with a thorough overview of the roles, the changing emphasis and the dynamic characteristics of the contemporary customer demand with a view to gaining a better understanding on the fundamental nature and variability of customer attributes. It is followed by a review of a number of well proven tools and techniques including QFD, HoQ, Affinity Diagram and AHP etc. on their applicability and effectiveness in organising, analysing and responding to dynamic customer requirements. Finally, an intelligent hybrid model amalgamating a variety of these techniques and a fuzzy inference sub-system is proposed to handle the diverse, ever-changing and often imprecise VoC. The proposed hybrid model is subsequently demonstrated in a practical case study.

In two previous reports we have investigated both standard and non-standard inferences in the presence of terminological cycles for the description logic EL, which allows for conjunctions, existential restrictions, and the top concept. Regarding standard inference problems, it was shown there that the subsumption problem remains polynomial for all three types of semantics usually considered for cyclic definitions in description logics, and that the instance problem remains polynomial for greatest fixpoint semantics. Regarding non-standard inference problems, it was shown that, w.r.t. greatest fixpoint semantics, the least common subsumer and the most specific concept always exist and can be computed in ploynomial time, and that, w.r.t. descriptive semantics, the least common subsumer need not exist. The present report is concerned with two problems left open by this previous work, namely the instance problem and the problem of computing most specific concepts w.r.t. descriptive semantics, which is the usual first-order semantics for description logic. We will show that the instance problem is polynomial also in this context. Similar to the case of the least common subsumer, the most specific concept w.r.t. descriptive semantics need not exist, but we are able to characterize the cases in which it exists and give a decidable sufficient condition for the existence of the most specific concept. Under this condition, it can be computed in polynomial time.

A maioria dos cursos universitários tem, em seu currículo, uma disciplina básica obrigatória de elementos de probabilidade e estatística. Além dos procedimentos de natureza descritiva, associados a análise de dados, fazem parte da ementa dessas disciplinas procedimentos inferenciais, geralmente apresentados dentro da teoria clássica(ou frequentista) de Neyman-Pearson. Não é costume nesta disciplina nem discutir aspectos epistemológicos ligados à inferência estatística e nem incluir a apresentação da escola Bayesiana, como uma possível alternativa. Sabidamente, tal disciplina é um entrave na vida escolar, tanto do aluno como do professor. Do aluno, porque este se depara, em boa parte das vezes, com um oferecimento mecânico da disciplina, sem motivação de natureza aplicada e sem vínculo aparente com sua realidade próxima curricular. Do professor, porque encontra geralmente alunos, além de despreparados com relação aos conceitos primários de incerteza e variabilidade, também com predisposição negativa, devido ao tabu associado à disciplina. Com o intuito de discutir a necessidade do oferecimento das primeiras noções inferenciais nessa disciplina, bem como responder a pergunta qual a inferência que deve ser ensinada numa disciplina básica de um curso universitário? buscamos caracterizar, ao longo de trabalho, as relações da estatística com: criação científica em geral e racionalismo e empirismo em particular; a existência ou não de um método científico; o objetivismo e o subjetivismo; os paradigmas das escolas clássica e Bayesiana; aprendizagem e cognição. Foram analisadas e comparadas as abordagens inferenciais feitas segundo cada escola, bem como apresentados alguns exemplos. A sugestão deste trabalho é de que o programa de uma primeira disciplina inclua os aspectos epistemológicos ligados à inferência, bem como a apresentação do tópico inferência estatística segundo as duas abordagens: clássica e Bayesiana. Isto eliminaria, pelo menos nos primeiros contatos do aluno com a área, a proposta de rompimento com a escola clássica preconizada por muitos adeptos da escola Bayesiana, bem como a proposta de resistência (manutenção do status quo), defendida por muitos elementos da escola clássica. Na verdade, a proposta preconiza a coexistência entre as duas escolas numa apresentação de curso básico, pois entendemos que o dever do professor é mostrar o estado da arte da área a seus alunos, deixando a opção (se isto fizer sentido) para uma etapa futura, seja acadêmica ou profissional. / In general most of the undergraduate courses in Brazil offer a basic discipline on probability and statistics. Beyond the descriptive procedures, associated with data analysis, these courses present to the students some inferential techniques, usually linked to the classical (frequentist) Neyman-Pearson school. It is not common to present the inferential aspects from the Bayesian point of view. Everybody knows that both student and teacher have problems with this basic discipline. The student, because he/she receives, in general, a mechanical course, without motivation, with no links to their other disciplines, and the teacher, because he/she usulally teaches to very naïve students concerning concept like uncertainty and variability. Added to that, students seem to have some fear towards the discipline (taboo). In order to discuss the first inferential notions presented in this discipline, and to answer the question which inference should we teach in a basic discipline of statistics to undergraduate students? we have tried, in this work, to characterise the relationship between statistics and the following aspects: scientific creation in general and empirism and rationalism in particular; the existence or not of a scientific method; objectivism and subjectivism; the paradigms associated to the classical and to the Bayesian schools; learning and some cognitive aspects. We have compared the inferential approaches, and some examples have been presented. This work suggests that the first program of a basic discipline of probability and statistics should include some epistemological inferential aspects as well as the introduction of inferential statistics by means of both approaches: classical and Bayesian. This action will prevent, at least at the first contact, the members of the Bayesian school from proposing the rupture with the classical, and also the members of the classical one from maintaining the status quo. In fact, the proposal is of coexistence of both schools in a first level, because we think it is a teachers duty to show the state of art to his/her students, giving the possibility of option (if necessary) for a following step.

A maioria dos cursos universitários tem, em seu currículo, uma disciplina básica obrigatória de elementos de probabilidade e estatística. Além dos procedimentos de natureza descritiva, associados a análise de dados, fazem parte da ementa dessas disciplinas procedimentos inferenciais, geralmente apresentados dentro da teoria clássica(ou frequentista) de Neyman-Pearson. Não é costume nesta disciplina nem discutir aspectos epistemológicos ligados à inferência estatística e nem incluir a apresentação da escola Bayesiana, como uma possível alternativa. Sabidamente, tal disciplina é um entrave na vida escolar, tanto do aluno como do professor. Do aluno, porque este se depara, em boa parte das vezes, com um oferecimento mecânico da disciplina, sem motivação de natureza aplicada e sem vínculo aparente com sua realidade próxima curricular. Do professor, porque encontra geralmente alunos, além de despreparados com relação aos conceitos primários de incerteza e variabilidade, também com predisposição negativa, devido ao tabu associado à disciplina. Com o intuito de discutir a necessidade do oferecimento das primeiras noções inferenciais nessa disciplina, bem como responder a pergunta qual a inferência que deve ser ensinada numa disciplina básica de um curso universitário? buscamos caracterizar, ao longo de trabalho, as relações da estatística com: criação científica em geral e racionalismo e empirismo em particular; a existência ou não de um método científico; o objetivismo e o subjetivismo; os paradigmas das escolas clássica e Bayesiana; aprendizagem e cognição. Foram analisadas e comparadas as abordagens inferenciais feitas segundo cada escola, bem como apresentados alguns exemplos. A sugestão deste trabalho é de que o programa de uma primeira disciplina inclua os aspectos epistemológicos ligados à inferência, bem como a apresentação do tópico inferência estatística segundo as duas abordagens: clássica e Bayesiana. Isto eliminaria, pelo menos nos primeiros contatos do aluno com a área, a proposta de rompimento com a escola clássica preconizada por muitos adeptos da escola Bayesiana, bem como a proposta de resistência (manutenção do status quo), defendida por muitos elementos da escola clássica. Na verdade, a proposta preconiza a coexistência entre as duas escolas numa apresentação de curso básico, pois entendemos que o dever do professor é mostrar o estado da arte da área a seus alunos, deixando a opção (se isto fizer sentido) para uma etapa futura, seja acadêmica ou profissional. / In general most of the undergraduate courses in Brazil offer a basic discipline on probability and statistics. Beyond the descriptive procedures, associated with data analysis, these courses present to the students some inferential techniques, usually linked to the classical (frequentist) Neyman-Pearson school. It is not common to present the inferential aspects from the Bayesian point of view. Everybody knows that both student and teacher have problems with this basic discipline. The student, because he/she receives, in general, a mechanical course, without motivation, with no links to their other disciplines, and the teacher, because he/she usulally teaches to very naïve students concerning concept like uncertainty and variability. Added to that, students seem to have some fear towards the discipline (taboo). In order to discuss the first inferential notions presented in this discipline, and to answer the question which inference should we teach in a basic discipline of statistics to undergraduate students? we have tried, in this work, to characterise the relationship between statistics and the following aspects: scientific creation in general and empirism and rationalism in particular; the existence or not of a scientific method; objectivism and subjectivism; the paradigms associated to the classical and to the Bayesian schools; learning and some cognitive aspects. We have compared the inferential approaches, and some examples have been presented. This work suggests that the first program of a basic discipline of probability and statistics should include some epistemological inferential aspects as well as the introduction of inferential statistics by means of both approaches: classical and Bayesian. This action will prevent, at least at the first contact, the members of the Bayesian school from proposing the rupture with the classical, and also the members of the classical one from maintaining the status quo. In fact, the proposal is of coexistence of both schools in a first level, because we think it is a teachers duty to show the state of art to his/her students, giving the possibility of option (if necessary) for a following step.