Online Handbook of Argumentation for AI: Volume 4
- URL: http://arxiv.org/abs/2401.09444v1
- Date: Wed, 20 Dec 2023 16:11:10 GMT
- Title: Online Handbook of Argumentation for AI: Volume 4
- Authors: Lars Bengel, Lydia Bl\"umel, Elfia Bezou-Vrakatseli, Federico
Castagna, Giulia D'Agostino, Isabelle Kuhlmann, Jack Mumford, Daphne
Odekerken, Fabrizio Russo, Stefan Sarkadi, Madeleine Waller, Andreas Xydis
- Abstract summary: This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI)
Argumentation as a field within artificial intelligence (AI) is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning.
OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.
- Score: 0.5318828099393893
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This volume contains revised versions of the papers selected for the fourth
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.
Related papers
- A Survey of Reasoning with Foundation Models [235.7288855108172]
Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
We introduce seminal foundation models proposed or adaptable for reasoning.
We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
arXiv Detail & Related papers (2023-12-17T15:16:13Z) - Generation of Explanations for Logic Reasoning [0.0]
The research is centred on employing GPT-3.5-turbo to automate the analysis of fortiori arguments.
This thesis makes significant contributions to the fields of artificial intelligence and logical reasoning.
arXiv Detail & Related papers (2023-11-22T15:22:04Z) - A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way.
Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
arXiv Detail & Related papers (2023-10-18T20:18:05Z) - Reasoning with Language Model Prompting: A Survey [86.96133788869092]
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications.
This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting.
arXiv Detail & Related papers (2022-12-19T16:32:42Z) - Online Handbook of Argumentation for AI: Volume 3 [1.5863918137497997]
This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI)
Argumentation as a field within artificial intelligence (AI) is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning.
OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.
arXiv Detail & Related papers (2022-12-15T17:49:44Z) - Characterising Research Areas in the field of AI [68.8204255655161]
We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics.
The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
arXiv Detail & Related papers (2022-05-26T16:30:30Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Online Handbook of Argumentation for AI: Volume 2 [0.20872491154425035]
This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI)
Argumentation as a field within artificial intelligence (AI) is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning.
OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.
arXiv Detail & Related papers (2021-06-16T13:34:13Z) - AR-LSAT: Investigating Analytical Reasoning of Text [57.1542673852013]
We study the challenge of analytical reasoning of text and introduce a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
We analyze what knowledge understanding and reasoning abilities are required to do well on this task.
arXiv Detail & Related papers (2021-04-14T02:53:32Z) - Systematic Mapping Study on the Machine Learning Lifecycle [4.4090257489826845]
The study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics.
We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
arXiv Detail & Related papers (2021-03-11T11:44:23Z) - Online Handbook of Argumentation for AI: Volume 1 [2.0620687400727093]
This volume contains revised versions of the papers selected for the first volume of the Online Handbook of Argumentation for AI (OHAAI)
Argumentation as a field within artificial intelligence (AI) is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning.
OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.
arXiv Detail & Related papers (2020-06-22T06:07:13Z)
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.