Tim@upenn.CSNET (Tim Finin) (06/18/86)
CIS Colloquium
3 p.m. Thursday, June 19, 1986
216 Moore School, University of Pennsylvania
MACHINE LEARNING: UNIFYING PRINCIPLES AND RECENT PROGRESS
Ryszard S. Michalski
University of Illinois
Machine learning, a field concerned with developing computational theories of
learning and constructing learning machines, is now one of the most active
research areas in artificial intelligence. An inference-based theory of
learning will be presented that unifies the basic learning strategies. Special
attention will be given to inductive learning strategies, which include
learning from examples and learning from observations and discovery.
We will show that inductive learning can be reviewed as a goal-oriented and
resource-constrained inference process. This process draws upon the learner's
background knowledge, and involves a novel type of inference rules, called
inductive inference rules. In contrast with truth-preserving deductive rules,
inductive rules are falsity-preserving.
Several projects conducted at our AI Laboratory at Illinois will be briefly
reviewed, and illustrated by examples from implemented programs.