Loading Now

Summary of Online Handbook Of Argumentation For Ai: Volume 4, by Lars Bengel et al.


Online Handbook of Argumentation for AI: Volume 4

by Lars Bengel, Lydia Blümel, Elfia Bezou-Vrakatseli, Federico Castagna, Giulia D’Agostino, Isabelle Kuhlmann, Jack Mumford, Daphne Odekerken, Fabrizio Russo, Stefan Sarkadi, Madeleine Waller, Andreas Xydis

First submitted to arxiv on: 20 Dec 2023

Categories

  • Main: Artificial Intelligence (cs.AI)
  • Secondary: None

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This revised volume of the Online Handbook of Argumentation for AI (OHAAI) features selected papers that explore formal theories of argument and argument interaction. Building on recent studies, this handbook provides a curated anthology of research on argumentation as a field within artificial intelligence (AI). It focuses on symbolic representations of knowledge and defeasible reasoning, making it relevant to researchers interested in the theoretical foundations of AI. The purpose of OHAAI is to serve as an open-access hub for tracking the latest PhD-driven research on theory and application of argumentation across various AI-related areas.
Low GrooveSquid.com (original content) Low Difficulty Summary
This handbook collects papers that explore how arguments work and interact with each other. It’s like a big book about how we can use computers to understand and make decisions based on reasons. The researchers who wrote these papers are trying to figure out how to make computers think more like humans do when we reason. This is important because it can help us create better artificial intelligence that can make smart choices.

Keywords

* Artificial intelligence  * Tracking