Justified Generalization: Acquiring Procedures from Examples

Item

Title: en_US Justified Generalization: Acquiring Procedures from Examples
Creator: en_US Andreae, Peter Merrett
Date: 2004-10-20T20:10:15Z
Date Available: 2004-10-20T20:10:15Z
Date Issued: en_US 1985-01-01
Identifier: en_US AITR-834
uri: http://hdl.handle.net/1721.1/6950
Abstract: en_US This thesis describes an implemented system called NODDY for acquiring procedures from examples presented by a teacher. Acquiring procedures form examples involves several different generalization tasks. Generalization is an underconstrained task, and the main issue of machine learning is how to deal with this underconstraint. The thesis presents two principles for constraining generalization on which NODDY is based. The first principle is to exploit domain based constraints. NODDY demonstrated how such constraints can be used both to reduce the space of possible generalizations to manageable size, and how to generate negative examples out of positive examples to further constrain the generalization. The second principle is to avoid spurious generalizations by requiring justification before adopting a generalization. NODDY demonstrates several different ways of justifying a generalization and proposes a way of ordering and searching a space of candidate generalizations based on how much evidence would be required to justify each generalization. Acquiring procedures also involves three types of constructive generalizations: inferring loops (a kind of group), inferring complex relations and state variables, and inferring predicates. NODDY demonstrates three constructive generalization methods for these kinds of generalization.
Extent: 21825077 bytes; 8257288 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-834

Item sets: AI Technical Reports (1964 - 2004)