A Representation Reducing Approach to Knowledge Discovery in Databases for Scientific Discovery
نویسنده
چکیده
Nordhausen and Langley’s IDS is a system for automated integrated scientific discovery. IDS’ is-a hierarchy both organizes knowledge and constrains search. This search bias, however, limits what may be discovered to knowledge learnable by operators that execute local tree manipulations on the is-a hierarchy. I present an alternative approach which uses representation reduction as the driving bias. This bias already is used by scientists and is motivated by Rissanen’s Minimum Description Length Criterion. Additionally, the bias allows search in the space of scientific models, not just behaviors. I also present a meta-learning technique whereby the program may become more efficient at reducing representations over time.
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