Deductive and Abductive Reasoning with Causal and Evidential Information

G.M. Wieten; F.J. Bex; H. Prakken; S. Renooij

doi:10.3233/FAIA200522

Deductive and Abductive Reasoning with Causal and Evidential Information

G.M. Wieten
, F.J. Bex
, H. Prakken
, S. Renooij

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In this paper, we propose the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Moreover, we propose an argumentation formalism based on IGs that allows arguments to be formally evaluated.

Original language	English
Title of host publication	Computational Models of Argument
Subtitle of host publication	Proceedings of COMMA 2020
Editors	Henry Prakken, Stefano Bistarelli, Francesco Santini, Carlo Taticchi
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	383-394
ISBN (Electronic)	978-1-64368-107-8
ISBN (Print)	978-1-64368-106-1
DOIs	https://doi.org/10.3233/FAIA200522
Publication status	Published - 2020

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Publisher	IOS Press
Volume	326

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Deduction
Abduction
Causal and evidential information
Argumentation

Access to Document

10.3233/FAIA200522Licence: CC BY-NC

FAIA-326-FAIA200522Final published version, 425 KBLicence: CC BY-NC

http://ebooks.iospress.nl/volumearticle/55388Licence: CC BY-NC

Cite this

Wieten, G. M., Bex, F. J., Prakken, H., & Renooij, S. (2020). Deductive and Abductive Reasoning with Causal and Evidential Information. In H. Prakken, S. Bistarelli, F. Santini, & C. Taticchi (Eds.), Computational Models of Argument: Proceedings of COMMA 2020 (pp. 383-394). (Frontiers in Artificial Intelligence and Applications; Vol. 326). IOS Press. https://doi.org/10.3233/FAIA200522

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title = "Deductive and Abductive Reasoning with Causal and Evidential Information",

abstract = "In this paper, we propose the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Moreover, we propose an argumentation formalism based on IGs that allows arguments to be formally evaluated.",

keywords = "Deduction, Abduction, Causal and evidential information, Argumentation",

author = "G.M. Wieten and F.J. Bex and H. Prakken and S. Renooij",

year = "2020",

doi = "10.3233/FAIA200522",

language = "English",

isbn = "978-1-64368-106-1",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press",

pages = "383--394",

editor = "Henry Prakken and Stefano Bistarelli and Francesco Santini and Carlo Taticchi",

booktitle = "Computational Models of Argument",

address = "Netherlands",

}

Wieten, GM, Bex, FJ , Prakken, H & Renooij, S 2020, Deductive and Abductive Reasoning with Causal and Evidential Information. in H Prakken, S Bistarelli, F Santini & C Taticchi (eds), Computational Models of Argument: Proceedings of COMMA 2020. Frontiers in Artificial Intelligence and Applications, vol. 326, IOS Press, Amsterdam, pp. 383-394. https://doi.org/10.3233/FAIA200522

Deductive and Abductive Reasoning with Causal and Evidential Information. / Wieten, G.M.; Bex, F.J.; Prakken, H. et al.
Computational Models of Argument: Proceedings of COMMA 2020. ed. / Henry Prakken; Stefano Bistarelli; Francesco Santini; Carlo Taticchi. Amsterdam: IOS Press, 2020. p. 383-394 (Frontiers in Artificial Intelligence and Applications; Vol. 326).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Deductive and Abductive Reasoning with Causal and Evidential Information

AU - Wieten, G.M.

AU - Bex, F.J.

AU - Prakken, H.

AU - Renooij, S.

PY - 2020

Y1 - 2020

N2 - In this paper, we propose the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Moreover, we propose an argumentation formalism based on IGs that allows arguments to be formally evaluated.

AB - In this paper, we propose the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Moreover, we propose an argumentation formalism based on IGs that allows arguments to be formally evaluated.

KW - Deduction

KW - Abduction

KW - Causal and evidential information

KW - Argumentation

U2 - 10.3233/FAIA200522

DO - 10.3233/FAIA200522

M3 - Conference contribution

SN - 978-1-64368-106-1

T3 - Frontiers in Artificial Intelligence and Applications

SP - 383

EP - 394

BT - Computational Models of Argument

A2 - Prakken, Henry

A2 - Bistarelli, Stefano

A2 - Santini, Francesco

A2 - Taticchi, Carlo

PB - IOS Press

CY - Amsterdam

ER -

Deductive and Abductive Reasoning with Causal and Evidential Information

Abstract

Publication series

UN SDGs

Keywords

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Cite this