3 research outputs found
Probabilistic Reasoning with Abstract Argumentation Frameworks
Abstract argumentation offers an appealing way of representing and evaluating arguments
and counterarguments. This approach can be enhanced by considering probability
assignments on arguments, allowing for a quantitative treatment of formal argumentation.
In this paper, we regard the assignment as denoting the degree of belief that an agent
has in an argument being acceptable. While there are various interpretations of this, an
example is how it could be applied to a deductive argument. Here, the degree of belief that
an agent has in an argument being acceptable is a combination of the degree to which it
believes the premises, the claim, and the derivation of the claim from the premises. We
consider constraints on these probability assignments, inspired by crisp notions from classical
abstract argumentation frameworks and discuss the issue of probabilistic reasoning
with abstract argumentation frameworks. Moreover, we consider the scenario when assessments
on the probabilities of a subset of the arguments are given and the probabilities
of the remaining arguments have to be derived, taking both the topology of the argumentation
framework and principles of probabilistic reasoning into account. We generalise
this scenario by also considering inconsistent assessments, i.e., assessments that contradict
the topology of the argumentation framework. Building on approaches to inconsistency
measurement, we present a general framework to measure the amount of conflict of these
assessments and provide a method for inconsistency-tolerant reasoning