Deductive and Abductive Reasoning with Causal and Evidential Information

Publication date

2020

Authors

Wieten, RemiISNI 0000000492960331
Bex, FlorisORCID 0000-0002-5699-9656ISNI 0000000118066508
Prakken, HenryISNI 000000011466763X
Renooij, SiljaORCID 0000-0003-4339-8146ISNI 0000000396172124

Editors

Prakken, Henry
Bistarelli, Stefano
Santini, Francesco
Taticchi, Carlo

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

cc_by_nc

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, SDG 16 - Peace, Justice and Strong Institutions

Citation

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. Frontiers in Artificial Intelligence and Applications, vol. 326, IOS Press, Amsterdam, pp. 383-394. https://doi.org/10.3233/FAIA200522