2,154 research outputs found
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
Probabilistic Argumentation with Epistemic Extensions and Incomplete Information
Abstract argumentation offers an appealing way of representing and evaluating
arguments and counterarguments. This approach can be enhanced by a probability
assignment to each argument. There are various interpretations that can be
ascribed to this assignment. In this paper, we regard the assignment as
denoting the belief that an agent has that an argument is justifiable, i.e.,
that both the premises of the argument and the derivation of the claim of the
argument from its premises are valid. This leads to the notion of an epistemic
extension which is the subset of the arguments in the graph that are believed
to some degree (which we defined as the arguments that have a probability
assignment greater than 0.5). We consider various constraints on the
probability assignment. Some constraints correspond to standard notions of
extensions, such as grounded or stable extensions, and some constraints give us
new kinds of extensions
Probabilistic Argumentation. An Equational Approach
There is a generic way to add any new feature to a system. It involves 1)
identifying the basic units which build up the system and 2) introducing the
new feature to each of these basic units.
In the case where the system is argumentation and the feature is
probabilistic we have the following. The basic units are: a. the nature of the
arguments involved; b. the membership relation in the set S of arguments; c.
the attack relation; and d. the choice of extensions.
Generically to add a new aspect (probabilistic, or fuzzy, or temporal, etc)
to an argumentation network can be done by adding this feature to each
component a-d. This is a brute-force method and may yield a non-intuitive or
meaningful result.
A better way is to meaningfully translate the object system into another
target system which does have the aspect required and then let the target
system endow the aspect on the initial system. In our case we translate
argumentation into classical propositional logic and get probabilistic
argumentation from the translation.
Of course what we get depends on how we translate.
In fact, in this paper we introduce probabilistic semantics to abstract
argumentation theory based on the equational approach to argumentation
networks. We then compare our semantics with existing proposals in the
literature including the approaches by M. Thimm and by A. Hunter. Our
methodology in general is discussed in the conclusion
Computing the Grounded Semantics in all the Subgraphs of an Argumentation Framework: an Empirical Evaluation
Given an argumentation framework – with a finite set of arguments and the attack relation identifying the graph – we study how the grounded labelling of a generic argument a varies in all the subgraphs of . Since this is an intractable problem of above-polynomial complexity, we present two non-naïve algorithms to find the set of all the subgraphs where the grounded semantic assigns to argument a specific label . We report the results of a series of empirical tests over graphs of increasing complexity. The value of researching the above problem is two-fold. First, knowing how an argument behaves in all the subgraphs represents strategic information for arguing agents. Second, the algorithms can be applied to the computation of the recently introduced probabilistic argumentation frameworks
Equilibrium States in Numerical Argumentation Networks
Given an argumentation network with initial values to the arguments, we look
for algorithms which can yield extensions compatible with such initial values.
We find that the best way of tackling this problem is to offer an iteration
formula that takes the initial values and the attack relation and iterates a
sequence of intermediate values that eventually converges leading to an
extension. The properties surrounding the application of the iteration formula
and its connection with other numerical and non-numerical techniques proposed
by others are thoroughly investigated in this paper
A probabilistic deontic argumentation framework
Régis Riveret: Conceptualization, Formal analysis, Validation, Writing - original draft, Writing - review & editing. Nir Oren: Validation, Writing - original draft, Writing - review & editing. Giovanni Sartor: Conceptualization, Validation, Writing - original draft, Writing - review & editing.Peer reviewedPostprin
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
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