Blackboard scheduler control knowledge for heuristic classification: Representation and inference

The scheduler is an key component of a blackboard system architecture. This thesis addressed the important problem of how to make the blackboard scheduler more knowledge intensive in a way that facilitates the acquisition, integration, and maintenance of the blackboard scheduler knowledge. The solution approach described in this thesis involved formulating the blackboard scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining, and maintaining the knowledge of a classification expert system are applicable to the blackboard scheduler knowledge.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Park, Young-Tack

Contributors dc:contributor

• Wilkins, David C.

• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1993 Park, Young-Tack

Language dc:language

eng

Identifier

AAI9314925
(UMI)AAI9314925

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F19670