Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Managing Uncertainty and Vagueness in Semantic Web

Ο Σημασιολογικός Ιστός στοχεύει στην διεκπεραίωση εργασιών σε υπολογιστικά συστήματα χωρίς την ανθρώπινη παρέμβαση. Προκειμένου να επιτευχθεί ο στόχος αυτός, εισάγεται η έννοια της πληροφορίας που είναι επεξεργάσιμη από μηχανές. Στα περισσότερα προβλήματα, η έννοια της πληροφορίας είναι συνυφασμένη με την έννοια της αβεβαιότητας και της ασάφειας. Και οι δύο έννοιες περιγράφονται με την κοινή ονομασία ατελής πληροφορία. Δεδομένου ότι ο Σημασιολογικός Ιστός απαρτίζεται από ένα σύνολο τεχνολογιών και των θεωριών που τις διέπουν, οποιαδήποτε μέθοδος αναπαράστασης θα πρέπει να βρίσκεται σε συμφωνία με άλλες υπάρχουσες. Συγκεκριμένα, το θεωρητικό πλαίσιο πρέπει να εντάσσεται ομαλά στη θεωρία που εφαρμόζεται στο Σημασιολογικό Ιστό. Η δε υλοποίησή του, ιδανικό είναι, να υποστηριχθεί με χρήση μεθόδων του Σημασιολογικού Ιστού, στις οποίες κυριαρχεί εκείνη των οντολογιών. Στη διατριβή μας, ορίσαμε μία μέθοδο αναπαράστασης της αβεβαιότητας και της ασάφειας μέσω ενός ενιαίου πλαισίου. Το μοντέλο Dempster-Shafer χρησιμοποιήθηκε για την αναπαράσταση της αβεβαιότητας και το μοντέλο Ασαφούς Λογικής και Ασαφών Συνόλων για την αναπαράσταση της ασάφειας. Για το λόγο αυτό, ορίσαμε το θεωρητικό πλαίσιο, στοχεύοντας σε ένα συνδυασμό ALC Λογικών Περιγραφών (Description Logics) με το μοντέλο Dempster-Shafer. Κατά τη διάρκεια της έρευνάς μας υλοποιήσαμε μεταοντολογίες για την αναπαράσταση της αβεβαιότητας και της ασάφειας και στη συνέχεια μελετήσαμε την συμπεριφορά τους σε πραγματικές εφαρμογές.Semantic Web has been designed for processing tasks without human intervention. In this context, the term machine processable information has been introduced. In most Semantic Web tasks, we come across information incompleteness issues, aka uncertainty and vagueness. For this reason, a method that represents uncertainty and vagueness under a common framework has to be defined. Semantic Web technologies are defined through a Semantic Web Stack and are based on a clear formal foundation. Therefore, any representation scheme should be aligned with these technologies and be formally defined. As the concept of ontologies is significant in the Semantic Web for representing knowledge, any framework is desirable to be built upon it. In our work, we have defined an approach for representing uncertainty and vagueness under a common framework. Uncertainty is represented through Dempster-Shafer model, whereas vagueness has been represented through Fuzzy Logic and Fuzzy Sets. For this reason, we have defined our theoretical framework, aimed at a combination of the classical crisp DL ALC with a Dempster-Shafer module. As a next step, we added fuzziness to this model. Throughout our work, we have implemented metaontologies in order to represent uncertain and vague concepts and, next, we have tested our methodology in real-world applications

Uncertainty Assessment in High-Risk Environments Using Probability, Evidence Theory and Expert Judgment Elicitation

The level of uncertainty in advanced system design is assessed by comparing the results of expert judgment elicitation to probability and evidence theory. This research shows how one type of monotone measure, namely Dempster-Shafer Theory of Evidence can expand the framework of uncertainty to provide decision makers a more robust solution space. The issues imbedded in this research are focused on how the relevant predictive uncertainty produced by similar action is measured. This methodology uses the established approach from traditional probability theory and Dempster-Shafer evidence theory to combine two classes of uncertainty, aleatory and epistemic. Probability theory provides the mathematical structure traditionally used in the representation of aleatory uncertainty. The uncertainty in analysis outcomes is represented by probability distributions and typically summarized as Complimentary Cumulative Distribution Functions (CCDFs). The main components of this research are probability of X in the probability theory compared to mx in evidence theory. Using this comparison, an epistemic model is developed to obtain the upper “CCPF - Complimentary Cumulative Plausibility Function” limits and the lower “CCBF - Complimentary Cumulative Belief Function” limits compared to the traditional probability function. A conceptual design for the Thermal Protection System (TPS) of future Crew Exploration Vehicles (CEV) is used as an initial test case. A questionnaire is tailored to elicit judgment from experts in high-risk environments. Based on description and characteristics, the answers of the questionnaire produces information, that serves as qualitative semantics used for the evidence theory functions. The computational mechanism provides a heuristic approach for the compilation and presentation of the results. A follow-up evaluation serves as validation of the findings and provides useful information in terms of consistency and adoptability to other domains. The results of this methodology provide a useful and practical approach in conceptual design to aid the decision maker in assessing the level of uncertainty of the experts. The methodology presented is well-suited for decision makers that encompass similar conceptual design instruments

A Dempster-Shafer theory inspired logic.

Issues of formalising and interpreting epistemic uncertainty have always played a prominent role in Artificial Intelligence. The Dempster-Shafer (DS) theory of partial beliefs is one of the most-well known formalisms to address the partial knowledge. Similarly to the DS theory, which is a generalisation of the classical probability theory, fuzzy logic provides an alternative reasoning apparatus as compared to Boolean logic. Both theories are featured prominently within the Artificial Intelligence domain, but the unified framework accounting for all the aspects of imprecise knowledge is yet to be developed. Fuzzy logic apparatus is often used for reasoning based on vague information, and the beliefs are often processed with the aid of Boolean logic. The situation clearly calls for the development of a logic formalism targeted specifically for the needs of the theory of beliefs. Several frameworks exist based on interpreting epistemic uncertainty through an appropriately defined modal operator. There is an epistemic problem with this kind of frameworks: while addressing uncertain information, they also allow for non-constructive proofs, and in this sense the number of true statements within these frameworks is too large. In this work, it is argued that an inferential apparatus for the theory of beliefs should follow premises of Brouwer's intuitionism. A logic refuting tertium non daturìs constructed by defining a correspondence between the support functions representing beliefs in the DS theory and semantic models based on intuitionistic Kripke models with weighted nodes. Without addional constraints on the semantic models and without modal operators, the constructed logic is equivalent to the minimal intuitionistic logic. A number of possible constraints is considered resulting in additional axioms and making the proposed logic intermediate. Further analysis of the properties of the created framework shows that the approach preserves the Dempster-Shafer belief assignments and thus expresses modality through the belief assignments of the formulae within the developed logic

AN INTUITIVE INTERPRETATION OF THE THEORY OF EVIDENCE IN THE CONTEXT OF BIBLIOGRAPHICAL INDEXING

Models of bibliographical Indexing concern the construction of effective keyword taxonomies and the representation of relevance between document s and keywords. The theory of evidence concerns the elicitation and manipulation of degrees of belief rendered by multiple sources of evidence to a common set of propositions. The paper presents a formal framework in which adaptive taxonomies and probabilistic indexing are induced dynamically by the relevance opinions of the library's patrons. Different measures of relevance and mechanisms for combining them are presented and shown to be isomorphic to the belief functions and combination rules of the theory of evidence. The paper thus has two objectives: (i) to treat formally slippery concepts like probabilistic indexing and average relevance, and (ii) to provide an intuitive justification to the Dempster Shafer theory of evidence, using bibliographical indexing as a canonical example.Information Systems Working Papers Serie

Semantic Decision Support for Information Fusion Applications

La thèse s'inscrit dans le domaine de la représentation des connaissances et la modélisation de l'incertitude dans un contexte de fusion d'informations. L'idée majeure est d'utiliser les outils sémantiques que sont les ontologies, non seulement pour représenter les connaissances générales du domaine et les observations, mais aussi pour représenter les incertitudes que les sources introduisent dans leurs observations. Nous proposons de représenter ces incertitudes au travers d'une méta-ontologie (DS-ontology) fondée sur la théorie des fonctions de croyance. La contribution de ce travail porte sur la définition d'opérateurs d'inclusion et d'intersection sémantique et sur lesquels s'appuie la mise en œuvre de la théorie des fonctions de croyance, et sur le développement d'un outil appelé FusionLab permettant la fusion d'informations sémantiques à partir du développement théorique précédent. Une application de ces travaux a été réalisée dans le cadre d'un projet de surveillance maritime.This thesis is part of the knowledge representation domain and modeling of uncertainty in a context of information fusion. The main idea is to use semantic tools and more specifically ontologies, not only to represent the general domain knowledge and observations, but also to represent the uncertainty that sources may introduce in their own observations. We propose to represent these uncertainties and semantic imprecision trough a metaontology (called DS-Ontology) based on the theory of belief functions. The contribution of this work focuses first on the definition of semantic inclusion and intersection operators for ontologies and on which relies the implementation of the theory of belief functions, and secondly on the development of a tool called FusionLab for merging semantic information within ontologies from the previous theorical development. These works have been applied within a European maritime surveillance project.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF