A General Explanation-Based Learning Mechanism and Its Application to Narrative Understanding
Mooney, Raymond Joseph
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Permalink
https://hdl.handle.net/2142/69589
Description
Title
A General Explanation-Based Learning Mechanism and Its Application to Narrative Understanding
Author(s)
Mooney, Raymond Joseph
Issue Date
1988
Doctoral Committee Chair(s)
DeJong, Gerald F.
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
Abstract
Explanation-based learning (EBL) is a learning method which uses existing knowledge of the domain to construct an explanation for why a specific example is a member of a concept or why a specific combination of actions achieves a goal. This explanation is then generalized in an analytical manner in order to produce a general concept description or plan schema. Although a number of exploratory EBL systems which operate in particular domains have previously been constructed, recent research in this area has led to the development of general mechanisms which can perform explanation-based learning in a wide variety of domains.
This thesis describes a general EBL mechanism, EGGS, which can make use of declarative knowledge stored in the form of horn clauses, rewrite rules, or STRIPS operators. Numerous examples are presented illustrating its application to a wide variety of domains, including "blocks world" planning, logic circuit design, artifact recognition, and various forms of mathematical problem solving. The system is shown to improve its performance in each of these domains.
EGGS has been most thoroughly tested as a component of a narrative understanding system, GENESIS, which improves its own performance through learning. GENESIS processes short English narratives and constructs explanations for characters' intentional behavior. When the system detects that a character has achieved an important goal by combining actions in an unfamiliar way, EGGS is used to generalize the specific explanation for how the goal was achieved into a general plan schema. The resulting schema is then retained by the system and indexed into its existing knowledge-base. This schema can then be used to process narratives which were previously beyond the system's capabilities. The thesis also discusses GENESIS' ability to learn meanings for words related to its learned schemata and reviews several recent psychological experiments which demonstrate that GENESIS can be productively interpreted as a cognitive model of certain types of human learning.
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