Using logic programming for theory representation and scientific inference
(2021) In New Ideas in Psychology 61.- Abstract
The aim of this paper is to show that logic programming is a powerful tool for representing scientific theories and for scientific inference. In a logic program it is possible to encode the qualitative and quantitative components of a theory in first order predicate logic, which is a highly expressive formal language. A theory program can then be handed to an algorithm that reasons about the theory. We discuss the advantages of logic programming with regard to building formal theories and present a novel software package for scientific inference: Theory Toolbox. Theory Toolbox can derive any conclusions that are entailed by a theory, explain why a certain conclusion follows from a theory, and evaluate a theory with regard to its... (More)
The aim of this paper is to show that logic programming is a powerful tool for representing scientific theories and for scientific inference. In a logic program it is possible to encode the qualitative and quantitative components of a theory in first order predicate logic, which is a highly expressive formal language. A theory program can then be handed to an algorithm that reasons about the theory. We discuss the advantages of logic programming with regard to building formal theories and present a novel software package for scientific inference: Theory Toolbox. Theory Toolbox can derive any conclusions that are entailed by a theory, explain why a certain conclusion follows from a theory, and evaluate a theory with regard to its internal coherence and generalizability. Because logic is, or should be, a cornerstone of scientific practice, we believe that our paper can make an important contribution to scientific psychology.
(Less)
- author
- Rohner, Jean Christophe LU and Kjellerstrand, Håkan
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Logic programming, Prolog, Scientific inference, Scientific rigor
- in
- New Ideas in Psychology
- volume
- 61
- article number
- 100838
- publisher
- Elsevier
- external identifiers
-
- scopus:85097761278
- ISSN
- 0732-118X
- DOI
- 10.1016/j.newideapsych.2020.100838
- language
- English
- LU publication?
- yes
- id
- 84dc841c-461c-41d9-8b14-69379f239456
- date added to LUP
- 2021-01-04 10:37:53
- date last changed
- 2022-04-26 22:58:04
@article{84dc841c-461c-41d9-8b14-69379f239456,
abstract = {{<p>The aim of this paper is to show that logic programming is a powerful tool for representing scientific theories and for scientific inference. In a logic program it is possible to encode the qualitative and quantitative components of a theory in first order predicate logic, which is a highly expressive formal language. A theory program can then be handed to an algorithm that reasons about the theory. We discuss the advantages of logic programming with regard to building formal theories and present a novel software package for scientific inference: Theory Toolbox. Theory Toolbox can derive any conclusions that are entailed by a theory, explain why a certain conclusion follows from a theory, and evaluate a theory with regard to its internal coherence and generalizability. Because logic is, or should be, a cornerstone of scientific practice, we believe that our paper can make an important contribution to scientific psychology.</p>}},
author = {{Rohner, Jean Christophe and Kjellerstrand, Håkan}},
issn = {{0732-118X}},
keywords = {{Logic programming; Prolog; Scientific inference; Scientific rigor}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{New Ideas in Psychology}},
title = {{Using logic programming for theory representation and scientific inference}},
url = {{http://dx.doi.org/10.1016/j.newideapsych.2020.100838}},
doi = {{10.1016/j.newideapsych.2020.100838}},
volume = {{61}},
year = {{2021}},
}