Student modeling based on fuzzy inference mechanisms

The paper presents a competence-based instructional design system and a way to provide a personalization of navigation in the course content. The navigation aid tool builds on the competence graph and the student model, which includes the elements of uncertainty in the assessment of students. An individualized navigation graph is constructed for each student, suggesting the competences the student is more prepared to study. We use fuzzy set theory for dealing with uncertainty. The marks of the assessment tests are transformed into linguistic terms and used for assigning values to linguistic variables. For each competence, the level of difficulty and the level of knowing its prerequisites are calculated based on the assessment marks. Using these linguistic variables and approximate reasoning (fuzzy IF-THEN rules), a crisp category is assigned to each competence regarding its level of recommendation.

Mètodes algebraics i de teoria de la prova per a la formalització del raonament vague

Projecte de recerca elaborat a partir d’una estada a la Università degli studi di Siena, Italy , entre 2007 i 2009. El projecte ha consistit en un estudi de la formalització lògica del raonament en presència de vaguetat amb els mètodes de la Lògica Algebraica i de la Teoria de la Prova. S'ha treballat fonamental en quatre direccions complementàries. En primer lloc, s'ha proposat un nou plantejament, més abstracte que el paradigma dominant fins ara, per l'estudi dels sistemes de lògica borrosa. Fins ara en l'estudi d'aquests sistemes l'atenció havia recaigut essencialment en l'obtenció de semàntiques basades en tnormes contínues (o almenys contínues per l'esquerra). En primer nivell de major abstracció hem estudiat les propietats de completesa de les lògiques borroses (tant proposicionals com de primer ordre) respecte de semàntiques definides sobre qualsevol cadena de valors de veritat, no necessàriament només sobre l'interval unitat dels nombres reals. A continuació, en un nivell encara més abstracte, s’ha pres l'anomenada jerarquia de Leibniz de la Lògica Algebraica Abstracta que classifica tots els sistemes lògics amb un bon comportament algebraic i s'ha expandit a una nova jerarquia (que anomenem implicacional) que permet definir noves classes de lògiques borroses que contenen quasi totes les conegudes fins ara. En segon lloc, s’ha continuat una línia d'investigació iniciada els darrers anys consistent en l'estudi de la veritat parcial com a noció sintàctica (és a dir, com a constants de veritat explícites en els sistemes de prova de les lògiques borroses). Per primer cop, s’ha considerat la semàntica racional per les lògiques proposicionals i la semàntica real i racional per les lògiques de primer ordre expandides amb constants. En tercer lloc, s’ha tractat el problema més fonamental del significat i la utilitat de les lògiques borroses com a modelitzadores de (part de) els fenòmens de la vaguetat en un darrer article de caràcter més filosòfic i divulgatiu, i en un altre més tècnic en què defensem la necessitat i presentem l'estat de l'art de l'estudi de les estructures algèbriques associades a les lògiques borroses. Finalment, s’ha dedicat la darrera part del projecte a l'estudi de la complexitat aritmètica de les lògiques borroses de primer ordre.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Adding similarity-based reasoning capabilities to a Horn fragment of possibilistic logic with fuzzy constants

PLFC is a first-order possibilistic logic dealing with fuzzy constants and fuzzily restricted quantifiers. The refutation proof method in PLFC is mainly based on a generalized resolution rule which allows an implicit graded unification among fuzzy constants. However, unification for precise object constants is classical. In order to use PLFC for similarity-based reasoning, in this paper we extend a Horn-rule sublogic of PLFC with similarity-based unification of object constants. The Horn-rule sublogic of PLFC we consider deals only with disjunctive fuzzy constants and it is equipped with a simple and efficient version of PLFC proof method. At the semantic level, it is extended by equipping each sort with a fuzzy similarity relation, and at the syntactic level, by fuzzily “enlarging” each non-fuzzy object constant in the antecedent of a Horn-rule by means of a fuzzy similarity relation.

A logic programming framework for possibilistic argumentation: formalization and logical properties

In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. The logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper presents Possibilistic Logic Programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on G¨odel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program P by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studied