1,149 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
An Imprecise Probability Approach for Abstract Argumentation based on Credal Sets
Some abstract argumentation approaches consider that arguments have a degree
of uncertainty, which impacts on the degree of uncertainty of the extensions
obtained from a abstract argumentation framework (AAF) under a semantics. In
these approaches, both the uncertainty of the arguments and of the extensions
are modeled by means of precise probability values. However, in many real life
situations the exact probabilities values are unknown and sometimes there is a
need for aggregating the probability values of different sources. In this
paper, we tackle the problem of calculating the degree of uncertainty of the
extensions considering that the probability values of the arguments are
imprecise. We use credal sets to model the uncertainty values of arguments and
from these credal sets, we calculate the lower and upper bounds of the
extensions. We study some properties of the suggested approach and illustrate
it with an scenario of decision making.Comment: 8 pages, 2 figures, Accepted in The 15th European Conference on
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU
2019
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
Extending Modular Semantics for Bipolar Weighted Argumentation (Technical Report)
Weighted bipolar argumentation frameworks offer a tool for decision support
and social media analysis. Arguments are evaluated by an iterative procedure
that takes initial weights and attack and support relations into account. Until
recently, convergence of these iterative procedures was not very well
understood in cyclic graphs. Mossakowski and Neuhaus recently introduced a
unification of different approaches and proved first convergence and divergence
results. We build up on this work, simplify and generalize convergence results
and complement them with runtime guarantees. As it turns out, there is a
tradeoff between semantics' convergence guarantees and their ability to move
strength values away from the initial weights. We demonstrate that divergence
problems can be avoided without this tradeoff by continuizing semantics.
Semantically, we extend the framework with a Duality property that assures a
symmetric impact of attack and support relations. We also present a Java
implementation of modular semantics and explain the practical usefulness of the
theoretical ideas
Towards a Computational Analysis of Probabilistic Argumentation Frameworks
In this paper we analyze probabilistic argumentation frameworks (PAFs), defined as an extension of Dung abstract argumentation frameworks in which each argument n is asserted with a probability p(n). The debate around PAFs has so far centered on their theoretical definition and basic properties. This work contributes to their computational analysis by proposing a first recursive algorithm to compute the probability of acceptance of each argument under grounded and preferred semantics, and by studying the behavior of PAFs with respect to reinstatement, cycles and changes in argument structure. The computational tools proposed may provide strategic information for agents selecting the next step in an open argumentation process and they represent a contribution in the debate about gradualism in abstract argumentation
Norms of public argumentation and the ideals of correctness and participation
Argumentation as the public exchange of reasons is widely thought to enhance deliberative interactions that generate and justify reasonable public policies. Adopting an argumentation-theoretic perspective, we survey the norms that should govern public argumentation and address some of the complexities that scholarly treatments have identified. Our focus is on norms associated with the ideals of correctness and participation as sources of a politically legitimate deliberative outcome. In principle, both ideals are mutually coherent. If the information needed for a correct deliberative outcome is distributed among agents, then maximising participation increases information diversity. But both ideals can also be in tension. If participants lack competence or are prone to biases, a correct deliberative outcome requires limiting participation. The central question for public argumentation, therefore, is how to strike a balance between both ideals. Rather than advocating a preferred normative framework, our main purpose is to illustrate the complexity of this theme
A Filtering-based General Approach to Learning Rational Constraints of Epistemic Graphs
Epistemic graphs generalize the epistemic approach to probabilistic
argumentation and tackle the uncertainties in and between arguments. A
framework was proposed to generate epistemic constraints from data using a
two-way generalization method in the perspective of only considering the
beliefs of participants without considering the nature of relations represented
in an epistemic graph. The deficiency of original framework is that it is
unable to learn rules using tighter constraints, and the learnt rules might be
counterintuitive. Meanwhile, when dealing with more restricted values, the
filtering computational complexity will increase sharply, and the time
performance would become unreasonable. This paper introduces a filtering-based
approach using a multiple-way generalization step to generate a set of rational
rules based on both the beliefs of each agent on different arguments and the
epistemic graph corresponding to the epistemic constraints. This approach is
able to generated rational rules with multiple restricted values in higher
efficiency. Meanwhile, we have proposed a standard to analyze the rationality
of a dataset based on the postulates of deciding rational rules. We evaluate
the filtering-based approach on two suitable data bases. The empirical results
show that the filtering-based approach performs well with a better efficiency
comparing to the original framework, and rules generated from the improved
approach are ensured to be rational.Comment: 19 pages, 9 figures, submitted to SAC 202
Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation
In the constellations approach to probabilistic argumentation,
there is a probability distribution over the subgraphs of an
argument graph, and this can be used to represent the uncertainty in the structure of the argument graph. In this paper, we consider how we can construct this probability distribution from data. We provide a language for data based
on perspectives (opinions) on the structure of the graph, and
we introduce a framework (based on general properties and
some specific proposals) for aggregating these perspectives,
and as a result obtaining a probability distribution that best
reflects these perspectives. This can be used in applications
such as summarizing collections of online reviews and combining conflicting reports
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