On the Inferential Zigzag and Its Activation Towards Clarifying What It Is Commonsense Reasoning

This paper has a twofold goal: The first is to study how the inferential zigzag can be activated, even computationally, trying to analyse what kind of reasoning consists of, where its ’mechanism’ is rooted, how it can be activated since without all this it can just seem a metaphysical idea. The second, not so deeply different - as it can be presumed at a first view - but complementary, is to explore the subject’s link with the old thought on conjectures of the 15th Century Theologist and Philosopher Nicolaus Cusanus who was the first thinker consciously and extensively using conjectures

Bornological structures on many-valued sets

We introduce an approach to the concept of bornology in the framework of many-valued mathematical structures and develop the basics of the theory of many-valued bornological spaces and initiate the study of the category of many-valued bornological spaces and appropriately defined bounded "mappings" of such spaces. A scheme for constructing many-valued bornologies with prescribed properties is worked out. In particular, this scheme allows to extend an ordinary bornology of a metric space to a many-valued bornology on it

‎On the Inferential Zigzag and Its Activation Towards Clarifying What It Is Commonsense Reasoning

This paper has a twofold goal‎: ‎The first is to study how the inferential zigzag can be activated‎, ‎even computationally‎, ‎trying to analyse what kind of reasoning consists of‎, ‎where its 'mechanism' is rooted‎, ‎how it can be activated since without all this it can just seem a metaphysical idea‎. ‎The second‎, ‎not so deeply different‎ - ‎as it can be presumed at a first view‎ - ‎but complementary‎, ‎is to explore the subject's link with the old thought on conjectures of the 15th Century Theologist and Philosopher Nicolaus Cusanus‎ ‎who was the first thinker consciously and extensively using conjectures‎

Tracking the consequences of design decisions in mechatronic systems engineering

19 pagesInternational audienceThe design of mechatronic systems involves several technical and scientific disciplines. It is often difficult to anticipate, at the outset, the consequences of design decisions on the ultimate effectiveness of such complex systems, in which case the evaluation process is required to support the designers each time engineering choices must be made or justified. Since designers may belong to different technical and scientific cultures however, their understanding of both the design stakes and the evaluation process is too often biased. Moreover, design choices take place in an uncertain context and according to multiple criteria, some of which may be contradictory. In order to track the consequences of design decisions, we are proposing a conceptual data model to perform evaluations within the MBSE framework of Systems Engineering. We then proceed by relying on the relationships demonstrated by such a model to identify the potential impacts of design choices on future product performance. Since data available during the conceptual phase of the design are typically uncertain or imprecise, an original research protocol is extended to a qualitative impact analysis for the purpose of highlighting the most promising alternative system design solutions (ASDS). An example in the mechatronics field serves to illustrate our proposals

Label Ranking with Probabilistic Models

Diese Arbeit konzentriert sich auf eine spezielle Prognoseform, das sogenannte Label Ranking. Auf den Punkt gebracht, kann Label Ranking als eine Erweiterung des herkömmlichen Klassifizierungproblems betrachtet werden. Bei einer Anfrage (z. B. durch einen Kunden) und einem vordefinierten Set von Kandidaten Labels (zB AUDI, BMW, VW), wird ein einzelnes Label (zB BMW) zur Vorhersage in der Klassifizierung benötigt, während ein komplettes Ranking aller Label (zB BMW> VW> Audi) für das Label Ranking erforderlich ist. Da Vorhersagen dieser Art, bei vielen Problemen der realen Welt nützlich sind, können Label Ranking-Methoden in mehreren Anwendungen, darunter Information Retrieval, Kundenwunsch Lernen und E-Commerce eingesetzt werden. Die vorliegende Arbeit stellt eine Auswahl an Methoden für Label-Ranking vor, die Maschinelles Lernen mit statistischen Bewertungsmodellen kombiniert. Wir konzentrieren wir uns auf zwei statistische Ranking-Modelle, das Mallows- und das Plackett-Luce-Modell und zwei Techniken des maschinellen Lernens, das Beispielbasierte Lernen und das Verallgemeinernde Lineare Modell

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option