Blackboard scheduler control knowledge for heuristic classification: Representation and inference
Park, Young-Tack
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/19670
Description
Title
Blackboard scheduler control knowledge for heuristic classification: Representation and inference
Author(s)
Park, Young-Tack
Issue Date
1993
Doctoral Committee Chair(s)
Wilkins, David C.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Computer Science
Language
eng
Abstract
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.
In this thesis, the MINERVA expert system shell was extended by the addition of a blackboard scheduler level. The problem solving cycle involves a deliberation phase, wherein all the heuristic classification strategies that are applicable are collected. This is followed by a scheduling phase wherein the classification expert system for scheduling automatically gathers evidence for and against each of the applicable strategic actions, thereby ranking them according to desirability. Finally, there is an action phase that executes the most highly ranked strategic task.
One important innovation of this research is that of recursive heuristic classification: this thesis demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as a heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification: the classification alternatives that are selected among are dynamically generated in real-time and then evidence is gathered for and against these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of preenumerated fixed alternatives.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
