Attributional Calculus: A Logic and Representation Language for Natural Induction

Simple item page
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:26Z
dc.date.available2006-11-03T18:17:26Z
dc.date.issued2004-04
dc.description.abstractAttributional calculus (AC) is a typed logic system that combines elements of propositional logic, predicate calculus, and multiple-valued logic for the purpose of natural induction. By natural induction is meant a form of inductive learning that generates hypotheses in human-oriented forms, that is, forms that appear natural to people, and are easy to understand and relate to human knowledge. To serve this goal, AC includes non-conventional logic operators and forms that can make logic expressions simpler and more closely related to the equivalent natural language descriptions. AC has two forms, basic and extended, each of which can be bare or annotated. The extended form adds more operators to the basic form, and the annotated form includes parameters characterizing statistical properties of bare expressions. AC has two interpretation schemas, strict and flexible. The strict schema interprets AC expressions as true-false valued, and the flexible schema as continuously-valued. Conventional decision rules, association rules, decision trees, and n-of-m rules all can be viewed as special cases of attributional rules. Attributional rules can be directly translated to natural language, and visualized using concept association graphs and general logic diagrams. AC stems from Variable-Valued Logic 1 (VL1), and is intended to serve as a concept description language in advanced AQ inductive learning programs. To provide a motivation and background for AC the first part of the paper presents basic ideas and assumptions underlying concept learning.
dc.format.extent3080 bytes
dc.format.extent745440 bytes
dc.format.mimetypetext/xml
dc.format.mimetypeapplication/pdf
dc.identifier.citationMichalski, R. S., "Attributional Calculus: A Logic and Representation Language for Natural Induction," Reports of the Machine Learning and Inference Laboratory, MLI 04-2, George Mason University, Fairfax, VA, April, 2004.
dc.identifier.urihttps://hdl.handle.net/1920/1487
dc.language.isoen_US
dc.relation.ispartofseriesP 04-2
dc.relation.ispartofseriesMLI 04-2
dc.subjectInductive inference
dc.subjectMachine learning
dc.subjectNatural induction
dc.subjectData mining
dc.subjectPropositional calculus
dc.subjectPredicate logic
dc.subjectMany-valued logic
dc.subjectAttributional calculus
dc.titleAttributional Calculus: A Logic and Representation Language for Natural Induction
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
04-02.pdf
Size:
727.97 KB
Format:
Adobe Portable Document Format
Download

Collections

Machine Learning and Inference Laboratory, College of Public Health