A hierarchical representation of control knowledge for a heuristic classification shell
Najem, Ziad H.
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/2142/22326
Description
Title
A hierarchical representation of control knowledge for a heuristic classification shell
Author(s)
Najem, Ziad H.
Issue Date
1994
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)
Computer Science
Language
eng
Abstract
A central component of expertise is control knowledge which describes the approach to solving a problem. Human experts are capable of utilizing their control knowledge in different contexts. For example, an expert can utilize her diagnostic expertise in solving a problem, explaining the solution, or understanding how another expert is solving a problem (critiquing), etc. The objective of this thesis is a representation of control in a Heuristic Classification shell that can be used in problem solving and critiquing.
There are two components for control: Strategy knowledge defines the space of permissible operations at any instance during problem solving. Scheduling knowledge defines the selection criteria used by the system to select the next action to do. Critiquing is facilitated by having these two bodies of knowledge represented separately. Part of critiquing is to identify the problem-solving intentions behind the agent's observed actions. The use of control knowledge of an expert system during this step is facilitated by having the system stateless. A system is said to be stateless if its problem-solving operation is independent of any side effects produced while problem solving.
The major contribution of this thesis is the M scINERVA architecture for control knowledge. M scINERVA fully separates strategies for scheduling. The strategy layer is represented using declarative, abstract, and modular entities. The operation of the strategy layer is independent of any internal state. Thus, the M scINERVA architecture can be utilized in both problem solving and critiquing.
The control cycle of M scINERVA is deliberate-schedule-act. The strategy layer is fully deliberated to identify the list of all possible actions. Deliberation always starts from the given state of the problem. This ensures that the state of control is consistent with the problem and that control knowledge can be used for critiquing. However, full deliberation is computationally expensive when implemented using a regular P scROLOG interpreter. An additional contribution of this thesis is P scROTMS which is a new way of implementing P scROLOG interpreters. P scROTMS integrates a truth-maintenance system and a forward-reasoning system in addition to the regular P scROLOG query-interpreter. P scROTMS eliminates any unnecessary recomputation when a P scROLOG query is reevaluated.
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.
