A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers

Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of the true positive and false positive rate of the inferences of such models has been performed. Findings indicate a superior inferential capacity of the designed argument-based models

Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

A survey on managing users' preferences in ambient intelligence

Understanding the importance of preference management in ambient intelligent environments is key to providing systems that are better prepared to meet users' expectations. This survey provides an account of the various ways that preferences have been handled in Artificial Intelligence. Our analysis indicates that most of those techniques lack the ability to handle ambiguity and the evolution of preferences over time. Further exploration shows that argumentation can provide a feasible solution to complement existing work. We illustrate our claim by using an intelligent environment case study

Argumentation-Based Personal Assistants for Ambient Assisted Living

Personal assistants may help the elderly population to live independently and improve their welfare in ambient assisted living environments. However, although there are current proposals already developed both in academic and commercial domains, these systems are still far from being established on the daily lives of the general population. Argumentation technologies can help to deal with open challenges in this domain. In this chapter, we explore the connection between the related areas of argumentation, recommendation, decision-making and persuasion, and we review related work that can play an important role on the development of the next generation of personal assistants for ambient assisted living.Fil: Heras, Stella. Universidad Politécnica de Valencia; EspañaFil: Palanca, Javier. Universidad Politécnica de Valencia; EspañaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Claim Detection in Judgments of the EU Court of Justice

Mining arguments from text has recently become a hot topic in Artificial Intelligence. The legal domain offers an ideal scenario to apply novel techniques coming from machine learning and natural language processing, addressing this challenging task. Following recent approaches to argumentation mining in juridical documents, this paper presents two distinct contributions. The first one is a novel annotated corpus for argumentation mining in the legal domain, together with a set of annotation guidelines. The second one is the empirical evaluation of a recent machine learning method for claim detection in judgments. The method, which is based on Tree Kernels, has been applied to context-independent claim detection in other genres such as Wikipedia articles and essays. Here we show that this method also provides a useful instrument in the legal domain, especially when used in combination with domain-specific information

How to share knowledge by gossiping

International audienceGiven n agents each of which has a secret (a fact not known to anybody else), the classical version of the gossip problem is to achieve shared knowledge of all secrets in a minimal number of phone calls. There exist protocols achieving shared knowledge in 2(n−2) calls: when the protocol terminates everybody knows all the secrets. We generalize that problem and focus on higher-order shared knowledge: how many calls does it take to obtain that everybody knows that everybody knows all secrets? More generally, how many calls does it take to obtain shared knowledge of order k? This requires not only the communication of secrets, but also the communication of knowledge about secrets. We give a protocol that works in (k+1)(n−2) steps and prove that it is correct: it achieves shared knowledge of level k. The proof is presented in a dynamic epistemic logic that is based on the observability of propositional variables by agents