418 research outputs found
"Minimal defence": a refinement of the preferred semantics for argumentation frameworks
Dung's abstract framework for argumentation enables a study of the
interactions between arguments based solely on an ``attack'' binary relation on
the set of arguments. Various ways to solve conflicts between contradictory
pieces of information have been proposed in the context of argumentation,
nonmonotonic reasoning or logic programming, and can be captured by appropriate
semantics within Dung's framework. A common feature of these semantics is that
one can always maximize in some sense the set of acceptable arguments. We
propose in this paper to extend Dung's framework in order to allow for the
representation of what we call ``restricted'' arguments: these arguments should
only be used if absolutely necessary, that is, in order to support other
arguments that would otherwise be defeated. We modify Dung's preferred
semantics accordingly: a set of arguments becomes acceptable only if it
contains a minimum of restricted arguments, for a maximum of unrestricted
arguments.Comment: 8 pages, 3 figure
Graduality in Argumentation
Argumentation is based on the exchange and valuation of interacting
arguments, followed by the selection of the most acceptable of them (for
example, in order to take a decision, to make a choice). Starting from the
framework proposed by Dung in 1995, our purpose is to introduce 'graduality' in
the selection of the best arguments, i.e., to be able to partition the set of
the arguments in more than the two usual subsets of 'selected' and
'non-selected' arguments in order to represent different levels of selection.
Our basic idea is that an argument is all the more acceptable if it can be
preferred to its attackers. First, we discuss general principles underlying a
'gradual' valuation of arguments based on their interactions. Following these
principles, we define several valuation models for an abstract argumentation
system. Then, we introduce 'graduality' in the concept of acceptability of
arguments. We propose new acceptability classes and a refinement of existing
classes taking advantage of an available 'gradual' valuation
Characterisation of dinY, a new Escherichia coli DNA repair gene whose products are damage inducible even in a lexA(Def) background
Inference from controversial arguments
International audienceWe present new careful semantics within Dung's theory of argumentation. Under such careful semantics, two arguments cannot belong to the same extension whenever one of them indirectly attacks a third argument while the other one indirectly defends the third.We argue that our semantics lead to a better handling of controversial arguments than Dung's ones in some settings. We compare the careful inference relations induced by our semantics w.r.t. cautiousness; we also compare them with the inference relations induced by Dung's semantic
31. Carcinomes de primitif inconnu de localisation axillaire (CAPIax) : expérience du CHRU de Tours
Computing Argument Preferences and Explanations in Abstract Argumentation
Financial support from The UK Engineering and Physical Sciences Research Council (EPSRC) for the grant (EP/P011829/1), Supporting Security Policy with Effective Digital Intervention (SSPEDI) is gratefully acknowledged.Postprin
Towards Computational Persuasion via Natural Language Argumentation Dialogues
Computational persuasion aims to capture the human ability to persuade through argumentation for applications such as behaviour change in healthcare (e.g. persuading people to take more exercise or eat more healthily). In this paper, we review research in computational persuasion that incorporates domain modelling (capturing arguments and counterarguments that can appear in a persuasion dialogues), user modelling (capturing the beliefs and concerns of the persuadee), and dialogue strategies (choosing the best moves for the persuader to maximize the chances that the persuadee is persuaded). We discuss evaluation of prototype systems that get the userâs counterarguments by allowing them to select them from a menu. Then we consider how this work might be enhanced by incorporating a natural language interface in the form of an argumentative chatbot
Enhancing Goal-based Requirements Consistency: an Argumentation-based Approach
International audienceRequirements engineering research has for long recognized the leading role of goals as requirement artifacts during the requirements engineering specification processes. Given the large number of artifacts created during the requirements specification and the continuous evolution of these artifacts, reasoning about them remains a challenging task. Moreover, the rising complexity of the target domain under consideration during the requirements engineering process as well as the growth of geographically distributed projects explain why the number of collected requirements as well as their complexity also increase. In this context, providing support to stakeholders in achieving a common understanding of a set of goal-based requirements, in consolidating them and keeping them consistent over time is another challenging task. In this paper, we propose an approach to detect consistent sets of goal-based requirements and maintain their consistency over time. Our approach relies on argumentation theory which allows to detect the conflicts among elements called arguments. In particular, we rely on meta-argumentation, which instantiates abstract argumentation frameworks, where requirements are represented as arguments and the standard Dung-like argumentation framework is extended with additional relations between goal-based requirements
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