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

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

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

Reasoning about Uncertain Information and Conflict Resolution through Trust Revision

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Belief functions on MV-algebras of fuzzy sets: An overview

Belief functions are the measure theoretical objects Dempster-Shafer evidence theory is based on. They are in fact totally monotone capacities, and can be regarded as a special class of measures of uncertainty used to model an agent's degrees of belief in the occurrence of a set of events by taking into account different bodies of evidence that support those beliefs. In this chapter we present two main approaches to extending belief functions on Boolean algebras of events to MV-algebras of events, modelled as fuzzy sets, and we discuss several properties of these generalized measures. In particular we deal with the normalization and soft-normalization problems, and on a generalization of Dempster's rule of combination. © 2014 Springer International Publishing Switzerland.The authors also acknowledge partial support by the FP7-PEOPLE-2009-IRSES project MaToMUVI (PIRSES-GA-2009- 247584). Also, Flaminio acknowledges partial support of the Italian project FIRB 2010 (RBFR10DGUA-002), Kroupa has been supported by the grant GACR 13-20012S, and Godo acknowledges partial support of the Spanish projects EdeTRI (TIN2012-39348-C02-01) and Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010).Peer Reviewe

Advances and Applications of DSmT for Information Fusion

This book is devoted to an emerging branch of Information Fusion based on new approach for modelling the fusion problematic when the information provided by the sources is both uncertain and (highly) conflicting. This approach, known in literature as DSmT (standing for Dezert-Smarandache Theory), proposes new useful rules of combinations