Belief Revision in Structured Probabilistic Argumentation

In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates

Reasoning under uncertainty: the role of two informal fallacies in an emerging scientific inquiry

It is now commonplace in fallacy inquiry for many of the traditional informal fallacies to be viewed as reasonable or non-fallacious modes of argument. Central to this evaluative shift has been the attempt to examine traditional fallacies within their wider contexts of use. However, this pragmatic turn in fallacy evaluation is still in its infancy. The true potential of a contextual approach in the evaluation of the fallacies is yet to be explored. I examine how, in the context of scientific inquiry, certain traditional fallacies function by conferring epistemic gains upon inquiry. Specifically, I argue that these fallacies facilitate the progression of inquiry, particularly in the initial stages of inquiry when the epistemic context is one of uncertainty. The conception of these fallacies that emerges is that of heuristics of reasoning in contexts of epistemic uncertainty

Emerging infectious diseases: coping with uncertainty

The world’s scientific community must be in a state of constant readiness to address the threat posed by newly emerging infectious diseases. Whether the disease in question is SARS in humans or BSE in animals, scientists must be able to put into action various disease containment measures when everything from the causative pathogen to route(s) of transmission is essentially uncertain. A robust epistemic framework, which will inform decision-making, is required under such conditions of uncertainty. I will argue that this framework should have reasoning at its centre and, specifically, that forms of reasoning beyond deduction and induction should be countenanced by scientists who are confronted with emerging infectious diseases. In previous articles, I have presented a case for treating certain so-called traditional informal fallacies as rationally acceptable forms of argument that can facilitate scientific inquiry when little is known about an emerging disease. In this paper, I want to extend that analysis by highlighting the unique features of these arguments that makes them specially adapted to cope with conditions of uncertainty. Of course, such a view of the informal fallacies must at least be consistent with the reasoning practices of scientists, and particularly those scientists (viz. epidemiologists) whose task it is to track and respond to newly emerging infectious diseases. To this end, I draw upon examples of scientific reasoning from the UK’s BSE crisis, a crisis that posed a significant threat to both human and animal health

Informal fallacies as cognitive heuristics in public health reasoning

The public must make assessments of a range of health-related issues. However, these assessments require scientific knowledge which is often lacking or ineffectively utilized by the public. Lay people must use whatever cognitive resources are at their disposal to come to judgement on these issues. It will be contended that a group of arguments - so-called informal fallacies - are a valuable cognitive resource in this regard. These arguments serve as cognitive heuristics which facilitate reasoning when knowledge is limited or beyond the grasp of reasoners. The results of an investigation into the use of these arguments by the public are reported

Knowing with Experts: Contextual Knowledge in and Around Science

The original concept of epistemic dependence suggests uncritical deference to expert opinions for non-experts. In the light of recent work in science studies, however, the actual situation of epistemic dependence is seen to involve the necessary and ubiquitous need for lay evaluations of scientific experts. As expert knowledge means restricted cognitive access to some epistemic domain, lay evaluations of expert knowledge are rational and informed only when the criteria used by non-experts when judging experts are different from the criteria used by experts when making their claims. The distinction between ‘substantial knowledge’ and ‘contextual knowledge’ allows for the laypeople to know with experts without having to know precisely what experts know. Such meta-expert evaluations are not specific to the public sphere outside science, nor are they limited internally to science, but they are present in a wide range of contexts in and around science. The paper legitimizes the concept of contextual knowledge by relating it to the relevant literature, and expounds the idea by identifying some elements of such a knowledge

A Labelling Framework for Probabilistic Argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature

Syntactic Reasoning with Conditional Probabilities in Deductive Argumentation

Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence

Practical reasoning in political discourse: The UK government's response to the economic crisis in the 2008 Pre-Budget Report

This article focuses on practical reasoning in political discourse and argues for a better integration of argumentation theory with critical discourse analysis (CDA). Political discourse and its specific genres (for example, deliberation) primarily involve forms of practical reasoning, typically oriented towards finding solutions to problems and deciding on future courses of action. Practical reasoning is a form of inference from cognitive and motivational premises: from what we believe (about the situation or about means—end relations) and what we want or desire (our goals and values), leading to a normative judgement (and often a decision) concerning action. We offer an analysis of the main argument in the UK government’s 2008 Pre-Budget Report (HM Treasury, 2008) and suggest how a critical evaluation of the argument from the perspective of a normative theory of argumentation (particularly the informal logic developed by Douglas Walton) can provide the basis for an evaluation in terms of characteristic CDA concerns. We are advancing this analysis as a contribution to CDA, aimed at increasing the rigour and systematicity of its analyses of political discourse, and as a contribution to the normative concerns of critical social science

A first approach to combining ontologies and defeasible argumentation for the semantic web

The Semantic Web is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web through the use of ontology definitions. Problems for modelling common-sense reasoning (such as reasoning with uncertainty or with incomplete and potentially inconsistent information) are also present when defining ontologies. In recent years, defeasible argumentation has succeeded as an approach to formalize such common-sense reasoning. Agents operating in multi-agent systems in the context of the Semantic Web need to interact with each other in order to achieve the goals stated by their users. In this paper we propose a XML-based language named XDeLP for ontology interchange among agents in the web.Eje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI