Solving Power and Trust Conflicts through Argumentation in Agent-mediated Knowledge Distribution

Distributing pieces of knowledge in large, usually distributed organizations is a central problem in Knowledge and Organization management. Policies for distributing knowledge and information are mostly incomplete or in potential conflict with each other. As a consequence, decision processes for information distribution may be difficult to formalize on the basis of a rationally justified procedure. This article presents an argumentative approach to cope with this problem based on integrating the JITIK multiagent system with Defeasible Logic Programming (DeLP), a logic programming formalism for defeasible argumentation. We show how power relations, as well as delegation and trust, can be embedded within our framework in terms of DeLP, in such a way that a dialectical argumentation process works as a decision core. Conflicts among policies are solved on the basis of a dialectical analysis whose outcome determines to which specific users different pieces of knowledge are to be delivered.Fil: Chesñevar, Carlos Iván. Universitat de Lleida; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Brena, Ramón. Centro de Sistemas Inteligentes, Tecnológico de Monterrey; MéxicoFil: Aguirre, José L.. Centro de Sistemas Inteligentes, Tecnológico de Monterrey; Méxic

An Artificial Intelligence Approach to Dyscalculia

Dyscalculia stands for a brain-based condition that makes it hard to make sense of numbers and mathematical concepts. Some adolescents with dyscalculia cannot grasp basic number concepts. They work hard to learn and memorize basic number facts. They may know what to do in mathematical classes but do not understand why they are doing it. In other words, they miss the logic behind it. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work focuses on the development of an Intelligent System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming framework to Knowledge Representation and Reasoning, complemented with a Case-Based problem solving approach to computing, that allows for the handling of incomplete, unknown, or even contradictory information

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process

A Case Based Reasoning View of School Dropout Screening

The cause for student dropout is often termed as the antecedent of failure, since it stands for a key event, which leads to dropout. Indeed, school dropout is well thought out as one of the major worries of our times. It is a multi-layered and complex phenomenon, with many triggers, namely academic striving and failure, poor attendance, retention, disengagement from school or even socio-economic motives. School dropout represents economic and social losses to the individual, family and community. However, it may be prevented if the educational actors hold pro-active strategies (e.g., taking into account similar past experiences). Indeed, this work will start with the development of a decision support system to assess school dropout, centered on a formal framework based on Logic Programming for Knowledge Representation, complemented with a Case-Based Reasoning approach to problem solving, which caters for the handling of incomplete, unknown, or even contradictory information, i.e., it improves the analysis enactment of the retrieving cases process

International Standard ISO 9001 - An Artificial Intelligence View

ISO 9001 is recognized as a Quality Management Systems standard, i.e., it is the primary phase of a process of constant enhancement that will provide an organisation with the necessary management tools to improve working practices. Indeed, it provides a framework and a set of principles aimed at ensuring a common sense approach to the management of an organization in order to consistently satisfy customers and other stakeholders. Therefore, and in order to add value to ISO 9001, this work focuses on the development of a decision support system, which will allow companies to be able to meet the needs of customers by fulfilling requirements that reflect either the effectiveness or the non-effectiveness of an organization. The procedures for knowledge representation and reasoning used are based on an extension to the Logic Programming language, allowing the handling of incomplete, contradictory and even forbidden data, information and/or knowledge. The computational framework is centred on Artificial Neural Networks to evaluate customer’s satisfaction and the degree of confidence that one has on such a happening

Probabilistic Constraint Logic Programming

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl