A number of machine learning systems have been built which learn macro-operators or plan schemata, i.e. general compositions of actions which achieve a goal. However, previous research has not addressed the issue of generalizing the temporal order of operators and learning macro-operators with partially-ordered actions. This paper presents an algorithm for learning partially-ordered macro-operators which has been incorporated into the EGGS domain-independent explanation-based learning system. Examples from the domains of computer programming and narrative understanding are used to illustrate the performance of this system. These examples demonstrate that generalizing the order of operators can result in more general as well as more justified concepts. A theoretical analysis of the time complexity of the generalization algorithm is also presented.
ML ID: 209
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 lead 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.