1,581 research outputs found
Belief in attacks in epistemic probabilistic argumentation
The epistemic approach to probabilistic argumentation assigns belief to arguments. This is valuable in dialogical argumentation where one agent can model the beliefs another agent has in the arguments and this can be harnessed to make strategic choices of arguments to present. In this paper, we extend this epistemic approach by also representing the belief in attacks. We investigate properties of this proposal and compare it to the constellations approach showing neither subsumes the other
Argument-based Belief in Topological Structures
This paper combines two studies: a topological semantics for epistemic
notions and abstract argumentation theory. In our combined setting, we use a
topological semantics to represent the structure of an agent's collection of
evidence, and we use argumentation theory to single out the relevant sets of
evidence through which a notion of beliefs grounded on arguments is defined. We
discuss the formal properties of this newly defined notion, providing also a
formal language with a matching modality together with a sound and complete
axiom system for it. Despite the fact that our agent can combine her evidence
in a 'rational' way (captured via the topological structure), argument-based
beliefs are not closed under conjunction. This illustrates the difference
between an agent's reasoning abilities (i.e. the way she is able to combine her
available evidence) and the closure properties of her beliefs. We use this
point to argue for why the failure of closure under conjunction of belief
should not bear the burden of the failure of rationality.Comment: In Proceedings TARK 2017, arXiv:1707.0825
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
Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches
In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes in, and that a framework handling only attack relations is
sufficient to represent their opinions. Although it is natural to make these
assumptions as a starting point for further research, removing them or even
acknowledging that such removal should happen is more challenging for some of
these concepts than for others. Probabilistic argumentation is one of the
approaches that can be harnessed for more accurate user modelling. The
epistemic approach allows us to represent how much a given argument is believed
by a given person, offering us the possibility to express more than just three
agreement states. It is equipped with a wide range of postulates, including
those that do not make any restrictions concerning how initial arguments should
be viewed, thus potentially being more adequate for handling beliefs of the
people that have not fully disclosed their opinions in comparison to Dung's
semantics. The constellation approach can be used to represent the views of
different people concerning the structure of the framework we are dealing with,
including cases in which not all relations are acknowledged or when they are
seen differently than intended. Finally, bipolar argumentation frameworks can
be used to express both positive and negative relations between arguments. In
this paper we describe the results of an experiment in which participants
judged dialogues in terms of agreement and structure. We compare our findings
with the aforementioned assumptions as well as with the constellation and
epistemic approaches to probabilistic argumentation and bipolar argumentation
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
Belief Revision in Structured Probabilistic Argumentation
In real-world applications, knowledge bases consisting of all the information
at hand for a specific domain, along with the current state of affairs, are
bound to contain contradictory data coming from different sources, as well as
data with varying degrees of uncertainty attached. Likewise, an important
aspect of the effort associated with maintaining knowledge bases is deciding
what information is no longer useful; pieces of information (such as
intelligence reports) may be outdated, may come from sources that have recently
been discovered to be of low quality, or abundant evidence may be available
that contradicts them. In this paper, we propose a probabilistic structured
argumentation framework that arises from the extension of Presumptive
Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue
that this formalism is capable of addressing the basic issues of handling
contradictory and uncertain data. Then, to address the last issue, we focus on
the study of non-prioritized belief revision operations over probabilistic
PreDeLP programs. We propose a set of rationality postulates -- based on
well-known ones developed for classical knowledge bases -- that characterize
how such operations should behave, and study a class of operators along with
theoretical relationships with the proposed postulates, including a
representation theorem stating the equivalence between this class and the class
of operators characterized by the postulates
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
Updating probabilistic epistemic states in persuasion dialogues
In persuasion dialogues, the ability of the persuader to model the persuadee allows the persuader to make better choices of move. The epistemic approach to probabilistic argumentation is a promising way of modelling the persuadee’s belief in arguments, and proposals have been made for update methods that specify how these beliefs can be updated at each step of the dialogue. However, there is a need to better understand these proposals, and moreover, to gain insights into the space of possible update functions. So in this paper, we present a general framework for update functions in which we consider existing and novel update functions
Syntactic Reasoning with Conditional Probabilities in Deductive Argumentation
Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence
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