The SCC-recursiveness Principle in Fuzzy Argumentation Frameworks
- URL: http://arxiv.org/abs/2006.08880v1
- Date: Tue, 16 Jun 2020 02:33:06 GMT
- Title: The SCC-recursiveness Principle in Fuzzy Argumentation Frameworks
- Authors: Zongshun Wang and Jiachao Wu
- Abstract summary: SCC-recursiveness principle is a property of extensions which relies on the graph-theoretical notion of strongly connected components.
This paper is an exploration of the SCC-recursive theory in fuzzy argumentation frameworks (FAFs), which add numbers as fuzzy degrees to the arguments and attacks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dung's abstract argumentation theory plays a guiding role in the field of
formal argumentation. The properties of argumentation semantics have been
deeply explored in the previous literature. The SCC-recursiveness principle is
a property of the extensions which relies on the graph-theoretical notion of
strongly connected components. It provides a general recursive schema for
argumentation semantics, which is an efficient and incremental algorithm for
computing the argumentation semantics. However, in argumentation frameworks
with uncertain arguments and uncertain attack relation, the SCC-recursive
theory is absence. This paper is an exploration of the SCC-recursive theory in
fuzzy argumentation frameworks (FAFs), which add numbers as fuzzy degrees to
the arguments and attacks. In this paper, in order to extend the
SCC-recursiveness principle to FAFs, we first modify the reinstatement
principle and directionality principle to fit the FAFs. Then the
SCC-recursiveness principle in FAFs is formalized by the modified principles.
Additionally, some illustrating examples show that the SCC-recursiveness
principle also provides an efficient and incremental algorithm for simplify the
computation of argumentation semantics in FAFs.
Related papers
- SCC-recursiveness in infinite argumentation (extended version) [0.0]
SCC-recursiveness is a design principle in which the evaluation of arguments is decomposed according to strongly connected components.<n>We show that SCC-recursiveness fails to generalize reliably to infinite AFs due to issues with well-foundedness.<n>We then examine these semantics' behavior in finitary frameworks, where we find some of our semantics satisfy directionality.
arXiv Detail & Related papers (2025-07-09T13:57:12Z) - A Survey on Latent Reasoning [100.54120559169735]
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities.<n>CoT reasoning that verbalizes intermediate steps limits the model's expressive bandwidth.<n>Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state.
arXiv Detail & Related papers (2025-07-08T17:29:07Z) - CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
arXiv Detail & Related papers (2025-06-05T17:02:52Z) - A Methodology for Gradual Semantics for Structured Argumentation under Incomplete Information [15.717458041314194]
We provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks.
Our methodology accommodates incomplete information about arguments' premises.
We demonstrate the potential of our approach by introducing two different instantiations of the methodology.
arXiv Detail & Related papers (2024-10-29T16:38:35Z) - Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion.
It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing.
Our proposed method performs favorably against other state-of-the-art algorithms.
arXiv Detail & Related papers (2024-05-15T00:17:48Z) - Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation [12.308473939796945]
Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve dense tasks without laborious annotations.
The performance of WSSS, especially the stages of generating Class Activation Maps (CAMs) and refining pseudo masks, widely suffers from ambiguity.
We propose UniA, a unified single-staged WSSS framework, to tackle this issue from the perspective of uncertainty inference and affinity diversification.
arXiv Detail & Related papers (2024-04-12T01:54:59Z) - Open-Vocabulary Segmentation with Semantic-Assisted Calibration [73.39366775301382]
We study open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with contextual prior of CLIP.
We present a Semantic-assisted CAlibration Network (SCAN) to achieve state-of-the-art performance on open-vocabulary segmentation benchmarks.
arXiv Detail & Related papers (2023-12-07T07:00:09Z) - Ranking-based Argumentation Semantics Applied to Logical Argumentation
(full version) [2.9005223064604078]
We investigate the behaviour of ranking-based semantics for structured argumentation.
We show that a wide class of ranking-based semantics gives rise to so-called culpability measures.
arXiv Detail & Related papers (2023-07-31T15:44:33Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Admissibility in Strength-based Argumentation: Complexity and Algorithms
(Extended Version with Proofs) [1.5828697880068698]
We study the adaptation of admissibility-based semantics to Strength-based Argumentation Frameworks (StrAFs)
Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma.
We propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions.
arXiv Detail & Related papers (2022-07-05T18:42:04Z) - Rediscovering Argumentation Principles Utilizing Collective Attacks [26.186171927678874]
We extend the principle-based approach to Argumentation Frameworks with Collective Attacks (SETAFs)
Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs.
arXiv Detail & Related papers (2022-05-06T11:41:23Z) - Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation [66.85202434812942]
We reformulate few-shot segmentation as a semantic reconstruction problem.
We convert base class features into a series of basis vectors which span a class-level semantic space for novel class reconstruction.
Our proposed approach, referred to as anti-aliasing semantic reconstruction (ASR), provides a systematic yet interpretable solution for few-shot learning problems.
arXiv Detail & Related papers (2021-06-01T02:17:36Z) - Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
We introduce a novel variant called Semantic Contrastive Learning (SCL)
It explores the characteristics of both conventional contrastive learning and deep clustering.
It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
arXiv Detail & Related papers (2021-03-03T20:20:48Z) - On graded semantics of abstract argumentation: Extension-based case [0.0]
This paper considers some issues on extension-based semantics for abstract argumentation framework (AAF)
An alternative fundamental lemma is given, which generalizes the corresponding result obtained in [1].
A number of fundamental semantics for AAF, including conflict-free, admissible, complete and stable semantics, are shown to be closed under reduced meet modulo an ultrafilter.
arXiv Detail & Related papers (2020-12-19T04:32:19Z) - Closed-Form Factorization of Latent Semantics in GANs [65.42778970898534]
A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images.
In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner.
We propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
arXiv Detail & Related papers (2020-07-13T18:05:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.