Influence of Context on Decision Making during Requirements Elicitation

Requirements engineers should strive to get a better insight into decision making processes. During elicitation of requirements, decision making influences how stakeholders communicate with engineers, thereby affecting the engineers' understanding of requirements for the future information system. Empirical studies issued from Artificial Intelligence offer an adequate groundwork to understand how decision making is influenced by some particular contextual factors. However, no research has gone into the validation of such empirical studies in the process of collecting needs of the future system's users. As an answer, the paper empirically studies factors, initially identified by AI literature, that influence decision making and communication during requirements elicitation. We argue that the context's structure of the decision should be considered as a cornerstone to adequately study how stakeholders decide to communicate or not a requirement. The paper proposes a context framework to categorize former factors into specific families, and support the engineers during the elicitation process.Comment: appears in Proceedings of the 4th International Workshop on Acquisition, Representation and Reasoning with Contextualized Knowledge (ARCOE), 2012, Montpellier, France, held at the European Conference on Artificial Intelligence (ECAI-12

Defeasible Reasoning with Large Language Models - Initial Experiments and Future Directions

As Large Language Models gain prominence in the AI landscape, it is essential to understand their capabilities and limitations, among others in terms of reasoning. This paper is a first step towards understanding the capabilities in terms of defeasible rule-based reasoning. It presents results of initial experiments and discussed future research directions

Intelligent Tutorial System LISE

Development of information technology results in creation of more and more advanced teaching assisting systems. The authors of the article made an attempt to design and use the adaptative educational system based on the technologies: J2EE, JSF and XML. Unlike most hitherto solutions, the authors propose a three-tier architecture of the system and its improvement with additional functionalities

Interaction of arguments and values. Bridges between Artificial Intelligence and the Psychology of Reasoning

Los modelos de argumentación propuestos desde la Inteligencia Artificial ofrecen simplicidad y precisión para analizar la aceptabilidad de un argumento en interacción con otros. Sin embargo, se presentan dudas a la hora de ponderar su corrección, ya que el carácter, más dialéctico que lógico, de la argumentación impide contar con una semántica formal con la cual relacionarla. Aquí comentaremos los modelos de argumentación basada en valores de Gabbay y de Bench-Capon. Gabbay, por caso, busca implementar la intuición de que enfrentar argumentos que promueven un mismo valor (religioso, político, jurídico, etc.) es más efectivo que hacerlo desde un valor distinto no compartido. Valiéndome de algunos ejemplos tomados de la literatura, mostraré la importancia de tender puentes entre los modelos y datos empíricos que permitan contrastar dicha intuición. Argumentaré que hay problemas tanto conceptuales como representacionales que es necesario atacar, y señalaré algunas líneas de investigación experimental en tales direcciones.The argumentation models proposed from Artificial Intelligence offer simplicity and precision to analyze the acceptability of an argument in interaction with others. However, there are doubts when considering their correctness, since the character, more dialectical than logical, of the argumentation prevents having a formal semantics with which to relate it. Here we will discuss the value-based argumentation models by Gabbay and Bench-Capon. Gabbay, for instance, seeks to implement the intuition that confronting arguments that promote the same value (religious, political, legal, etc.) is more effective than doing it from a different, unshared value. Using some examples taken from the literature, I will show the importance of building bridges between the models and the empirical that enable to contrast such intuition. I will argue that there are both conceptual and representational problems that need to be addressed, and I will point out some lines of experimental research in these directions.Fil: Bodanza, Gustavo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Analysing online user activity to implicitly infer the mental workload of web-based tasks using defeasible reasoning

Mental workload can be considered the amount of cognitive load or effort used over time to complete a task in a complex system. Determining the limits of mental workload can assist in optimising designs and identify if user performance is affected by that design. Mental workload has also been presented as a defeasible concept, where one reason can defeat another and a 5-layer schema to represent domain knowledge to infer mental workload using defeasible reasoning has compared favourably to state-of-the-art inference techniques. Other previous work investigated using records of user activity for measuring mental workload at scale using web-based tasks For this research, a solution design and experiment were put together to analyse user activity from a web-based task to determine if mental workload can be inferred implicitly using defeasible reasoning. While there was one promising result, only weak correlation between inferred values and reference workload profile values was found