Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples

Minds and Machines 27 (1):37-77 (2017)
Michiel Van Lambalgen
University of Amsterdam
This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic normative contexts.
Keywords No keywords specified (fix it)
Categories (categorize this paper)
DOI 10.1007/s11023-017-9428-3
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

Our Archive

Upload a copy of this paper     Check publisher's policy     Papers currently archived: 35,507
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

View all 17 references / Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

Logical Calculi for Reasoning in the Presence of Uncertainty.Thomas J. Weigert - 1989 - Dissertation, University of Illinois at Chicago
Probabilistic Dynamic Epistemic Logic.Barteld P. Kooi - 2003 - Journal of Logic, Language and Information 12 (4):381-408.


Added to PP index

Total downloads
10 ( #521,820 of 2,287,633 )

Recent downloads (6 months)
2 ( #234,461 of 2,287,633 )

How can I increase my downloads?

Monthly downloads

My notes

Sign in to use this feature