Dempster-Shafer Τheory Application in Recommender Systems and Comparison of Constraint Programming’s and Möbius Transform’s Implementations

Στην πτυχιακή εργασία αυτή μας απασχολεί το θέμα του Χειρισμού Αβεβαιότητας στον τομέα της Αναπαράστασης Γνώσης χρησιμοποιώντας τη θεωρία των Dempster-Shafer. Σκοπός μας είναι να μελετήσουμε μια εφαρμογή της θεωρίας σε Συστήματα Συστάσεων και να μετρήσουμε την απόδοση του κανόνα του Dempster και του υπολογισμού εμπιστοσύνης χρησιμοποιώντας μια υλοποίηση της θεωρίας βασισμένη στον Λογικό Προγραμματισμό με Περιορισμούς. Ακόμη, επιθυμούμε να συγκρίνουμε την απόδοση της υλοποίησης βασισμένη στον Λογικό Προγραμματισμό με Περιορισμούς σε τυχαίες περιπτώσεις δοκιμής με μια υλοποίηση που χρησιμοποιεί μετασχηματισμούς Möbius. Σε γενικά πλαίσια ο υπολογιστικός χρόνος για την εφαρμογή υπήρξε λογικός. Όσον αφορά τη σύγκριση, και οι δύο περιπτώσεις είχαν θετικά και αρνητικά.In this thesis, we deal with the subject of Handling Uncertainty in the field of Knowledge Representation using Dempster-Shafer theory. Our goal is to study an application of Dempster-Shafer theory to Recommended Systems and measure the performance of Dempster's rule and belief computation when using an implementation that utilizes Constraint Logic Programming (CLP). Also, we aim to compare the performance of the CLP implementation on random test cases to the performance of an implementation of Dempster-Shafer theory using Möbius Transforms. In general, the computational time for the application was rational. Regarding the comparison, each implementations has its pros and cons

Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.Comment: Workshop on Theory of Belief Functions, Brest: France (2010

Representing Asymmetric Decision Problems Using Coarse Valuations

A valuation-based system approach to knowledge representation has shown its advantages in improving computational efficiency and in allowing many decision models including belief networks. This study applies the Dempster–Shafer theory of belief functions and extends its framework to allow coarse valuations, which admit incomplete specification of probabilities and utilities and, therefore, are more flexible in representing asymmetric decision problems. It presents an algorithm for making inferences and decisions in systems using coarse valuations. It shows that a coarse valuation-based system provides a most natural and compact representation of decision problems

Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids

Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster- Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme enhanced by geo-referenced maps used as an additional source of information, which is fused with a sensor grid. The paper presents the key stages of such a data fusion process. An adaptation of conjunctive combination rule is presented to refine the analysis of the conflicting information. The method uses temporal accumulation to make the distinction between stationary and mobile objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the occupied cells by differentiating, for instance, moving objects, parked cars, urban infrastructure and buildings. Experiments carried out on real- world data illustrate the benefits of such an approach.Comment: 6 pp. arXiv admin note: substantial text overlap with arXiv:1207.101