Proceedings:
Book One
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 3
Track:
Learning
Downloads:
Abstract:
Seven years ago, the AM program was constructed as an experiment in learning by discovery. Its source of power was a large body of heuristics, rules which guided it toward fruitful topics of investigation, toward profitable experiments to perform, toward plausible hypotheses and definitions. Other heuristics evaluated those discoveries for utility and "interestingness", and they were added to AM’s vocabulary of concepts. AM’s ultimate limitation apparently was due to its Inability to discover new, powerful, domain-specific heuristics for the various new fields it uncovered. At that time, it seemed straight-forward to simply add Heuretics (the study of heuristics) as one more field in which to let AM explore, observe, define, and develop. That task -- learning new heuristics by discovery -- turned out to be much more difficult than was realized initially, and we have just now achieved some successes at it. Along the way, it became clearer why AM had succeeded in the first place, and why it was so difficult to use the same paradigm to discover new heuristics. This paper discusses those recent insights. They spawn questions about "where the meaning really resides" in the concepts discovered by A.M. This leads to an appreciation of the crucial and unique role of representation in theory formation, a role involving the relationship between Form and Content.
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 3