46 research outputs found
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
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
ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]
We propose ProtoArgNet, a novel interpretable deep neural architecture for
image classification in the spirit of prototypical-part-learning as found, e.g.
in ProtoPNet. While earlier approaches associate every class with multiple
prototypical-parts, ProtoArgNet uses super-prototypes that combine
prototypical-parts into single prototypical class representations. Furthermore,
while earlier approaches use interpretable classification layers, e.g. logistic
regression in ProtoPNet, ProtoArgNet improves accuracy with multi-layer
perceptrons while relying upon an interpretable reading thereof based on a form
of argumentation. ProtoArgNet is customisable to user cognitive requirements by
a process of sparsification of the multi-layer perceptron/argumentation
component. Also, as opposed to other prototypical-part-learning approaches,
ProtoArgNet can recognise spatial relations between different
prototypical-parts that are from different regions in images, similar to how
CNNs capture relations between patterns recognized in earlier layers
Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)
Random forests are decision tree ensembles that can be used to solve a
variety of machine learning problems. However, as the number of trees and their
individual size can be large, their decision making process is often
incomprehensible. In order to reason about the decision process, we propose
representing it as an argumentation problem. We generalize sufficient and
necessary argumentative explanations using a Markov network encoding, discuss
the relevance of these explanations and establish relationships to families of
abductive explanations from the literature. As the complexity of the
explanation problems is high, we discuss a probabilistic approximation
algorithm and present first experimental results.Comment: Accepted for presentation at AAAI 2023. Contains appendix with proofs
and additional details about experiment