Dissertation Information for Jude William ShavlikNAME:
DEGREE:
DISCIPLINE:
SCHOOL: ADVISORS: COMMITTEE MEMBERS: MPACT Status: Incomplete - Not_Inspected Title: Generalizing the structure of explanations in explanation-based learning
Abstract: "Explanation-based learning is a recently developed approach to concept acquisition by computer. In this type of machine learning, a specific problem's solution is generalized into a form that can later be used to solve conceptually similar problems. A number of explanation-based generalization algorithms have been developed. Most do not alter the structure of the explanation of the specific problems--no additional objects nor inference rules are incorporated. Instead, these algorithms generalize by converting constants in the observed example to variables with constraints. However, many important concepts, in order to be properly learned, require that the structure of explanations be generalized. This can involve generalizing such things as the number of entities involved in a concept or the number of times some action is performed. For example, concepts such as momentum and energy conservation apply to arbitrary numbers of physical objects, clearing the top of a desk can require an arbitrary number of object relocations, and setting a table can involve an arbitrary number of guests. |
MPACT Scores for Jude William ShavlikA = 0 Advisors and Advisees Graphgenerating graph, please reload |
Students under Jude William Shavlik
ADVISEES:
- None
COMMITTEESHIPS:
- Yves Khawam - University of Wisconsin, Madison (1990)