Inductive Rule Learning on the Knowledge Level

TitleInductive Rule Learning on the Knowledge Level
Publication TypeJournal Article
Year of Publication2011
AuthorsSchmid, U., & Kitzelmann E.
Published inCognitive Systems Research
Other Numbers3214

We present an application of the analytical inductive programming system Igorto learning sets of recursive rules from positive experience. We propose that thisapproach can be used within cognitive architectures to model regularity detectionand generalization learning. Induced recursive rule sets represent the knowledgewhich can produce systematic and productive behavior in complex situations - thatis, control knowledge for chaining actions in different, but structural similar situations. We argue, that an analytical approach which is governed by regularity detection in example experience is more plausible than generate-and-test approaches.After introducing analytical inductive programming with Igor we will give a variety of example applications from different problem solving domains. Furthermore,we demonstrate that the same generalization mechanism can be applied to ruleacquisition for reasoning and natural language processing.


This work was partially funded by the Deutscher Akademischer Austausch Diesnst (DAAD) through a postdoctoral fellowship.

Bibliographic Notes

Cognitive Systems Research, Special Issue on Complex Cognition , Vol. 12, Issues 3-4, pp. 237-248

Abbreviated Authors

U. Schmid and E. Kitzelmann

ICSI Research Group


ICSI Publication Type

Article in journal or magazine