京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19339号 / 情博第591号 / 新制||情||103(附属図書館) / 32341 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 石田 亨, 教授 田中 克己, 教授 矢守 克也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20902号 / 理博第4354号 / 新制||理||1625(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 國廣 悌二, 教授 川合 光, 教授 杉本 茂樹 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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<p>Most existing classification algorithms either consider all features as equally important (equal weights), or do not analyze consistency of weights assigned to features. When features are not equally important, assigning consistent weights is a not obvious task. In general we have two cases. The first case assumes that a given sample of data does not contain any clue about the importance of features, so the weights are provided by a pool of experts and they are usually inconsistent. The second case assumes that the given sample contains some information about features importance, hence we can derive the weights directly from the sample. In this thesis we deal with both cases. Pairwise Comparisons and Weighted Support Vector Machines are used for the first case. For the second case a new approach based on the observation that the feature importance could be determined by the discrimination power of features has been proposed. For the first case, we start with pairwise comparisons to rank the importance of features, then we use distance-based inconsistency reduction to refine the weights assessment and make comparisons more precise. As the next step we calculate the weights through the fully-consistent or almost consistent pairwise comparison tables. For the second case, a novel concept of feature domain overlappings has been introduced. It can measure the feature discrimination power. This model is based on the assumption that less overlapping means more discrimination ability, and produces weights characterizing the importance of particular features. For both cases Weighted Support Vector Machines are used to classify the data. Both methods have been tested using two benchmark data sets, Iris and Vertebal.</p> <p>The results were especially superior to those obtained without weights.</p> / Master of Computer Science (MCS)

Recent American external activities directed towards Libya and Iran have brought to light the inconsistent nature of American foreign policy. This paper is essentially an investigation of these inconsistencies, attempting to illuminate through a multivariate time series analysis whether there are any overriding influences which can be used to explain such policy reversal and vacillation. The example of Israel was taken as a case study in foreign policy inconsistency, to underline the truly inconsistent nature of U.S. foreign policy while underlining the conventional explanations for such inconsistencies. The theoretical perspective considered that the policy influences fall into three essential categories, domestic (economic, political and public), external and idiosyncratic. The analysis followed the same format accounting for and operationalising each categorisation within the the model. To accurately facilitate the analysis an autoregressive model was used, taking a U.S. - World interaction data variable as the dependent variable throughout. A variety of economic, public, political and external variables were used as the input data. This analysis is a preliminary analysis offering suggestions and direction for future research. / M.A.

Errors in knowledge bases (KBs) written in a Description Logic (DL) are usually detected when reasoning derives an inconsistency or a consequence that does not hold in the application domain modelled by the KB. Whereas classical repair approaches produce maximal subsets of the KB not implying the inconsistency or unwanted consequence, optimal repairs maximize the consequence sets. In this paper, we extend previous results on how to compute optimal repairs from the DL EL to its extension EL⊥, which in contrast to EL can express inconsistency. The problem of how to deal with inconsistency in the context of optimal repairs was addressed previously, but in a setting where the (fixed) terminological part of the KB must satisfy a restriction on cyclic dependencies. Here, we consider a setting where this restriction is not required. We also show how the notion of optimal repairs obtained this way can be used in inconsistency- and error-tolerant reasoning.

Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.