justified argument
On the Justification of Statements in Argumentation-based Reasoning
Baroni, Pietro (Università degli Studi di Brescia) | Governatori, Guido (DATA61 and Commonwealth Scientific and Industrial Research Organisation (CSIRO)) | Lam, Ho-Pun (DATA61 and Commonwealth Scientific and Industrial Research Organisation (CSIRO)) | Riveret, Régis (DATA61 and Commonwealth Scientific and Industrial Research Organisation (CSIRO))
In the study of argumentation-based reasoning, argument justification has received far more attention than statement justification, often treated as a simple byproduct of the former. As a consequence, counterintuitive results and significant losses of sensitivity can be identified in the treatment of statement justification by otherwise appealing formalisms. To overcome this limitation, we propose to reappraise statement justification as a formalism-independent component. To this purpose, we introduce a novel general model of argumentation-based reasoning based on multiple levels of labellings, one of which is devoted to statement justification. This model is able to encompass several literature proposals as special cases: we illustrate this ability for the case of the ASPIC+ formalism and provide a first example of tunable statement justification in this context.
Justified Beliefs by Justified Arguments
Grossi, Davide (University of Liverpool) | Hoek, Wiebe van der (University of Liverpool)
The paper addresses how the information state of an agent relates to the arguments that the agent endorses. Information states are modeled in doxastic logic and arguments by recasting abstract argumentation theory in a modal logic format. The two perspectives are combined by an application of the theory of product logics, delivering sound and complete systems in which the interaction of arguments and beliefs is investigated.
Resolving Conflicting Arguments under Uncertainties
Ng, Benson Hin Kwong, Wong, Kam-Fai, Low, Boon-Toh
Distributed knowledge based applications in open domain rely on common sense information which is bound to be uncertain and incomplete. To draw the useful conclusions from ambiguous data, one must address uncertainties and conflicts incurred in a holistic view. No integrated frameworks are viable without an in-depth analysis of conflicts incurred by uncertainties. In this paper, we give such an analysis and based on the result, propose an integrated framework. Our framework extends definite argumentation theory to model uncertainty. It supports three views over conflicting and uncertain knowledge. Thus, knowledge engineers can draw different conclusions depending on the application context (i.e. view). We also give an illustrative example on strategical decision support to show the practical usefulness of our framework.