TY - JOUR
T1 - Learning one subprocedure per lesson
AU - VanLehn, Kurt
N1 - Funding Information:
I am deeply grateful to John Seely Brown for the ideas and encouragement he has lent to this project. I would like to thank the readers whose thoughtful comments have helped this document along: Agustin Araya, Tom Dietterich, John Laird, Stan Lanning, Steve Minton, Paul Rosen-bloom, and Dave Wilkins. This research was supported by the Personnel and Training Research Programs, Psychological Sciences Division, Office of Naval Research, under Contract No. N00014-82C-0067, Contract Authority Identification No. NR667-477.
PY - 1987/1
Y1 - 1987/1
N2 - sierra is a program that learns procedures incrementally from examples, where an example is a sequence of actions. sierra learns by completing explanations. Whenever the current procedure is inadequate for explaining (parsing) the current example, sierra formulates a new subprocedure whose instantiation completes the explanation (parse tree). The key to sierra's success lies in supplying a small amount of extra information with the examples. Instead of giving it a set of examples, under which conditions correct learning is provably impossible, it is given a sequence of "lessons," where a lesson is a set of examples that is guaranteed to introduce only one subprocedure. This permits unbiased learning, i.e., learning without a priori, heuristic preferences concerning the outcome.
AB - sierra is a program that learns procedures incrementally from examples, where an example is a sequence of actions. sierra learns by completing explanations. Whenever the current procedure is inadequate for explaining (parsing) the current example, sierra formulates a new subprocedure whose instantiation completes the explanation (parse tree). The key to sierra's success lies in supplying a small amount of extra information with the examples. Instead of giving it a set of examples, under which conditions correct learning is provably impossible, it is given a sequence of "lessons," where a lesson is a set of examples that is guaranteed to introduce only one subprocedure. This permits unbiased learning, i.e., learning without a priori, heuristic preferences concerning the outcome.
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U2 - 10.1016/0004-3702(87)90080-4
DO - 10.1016/0004-3702(87)90080-4
M3 - Article
AN - SCOPUS:0023151233
SN - 0004-3702
VL - 31
SP - 1
EP - 40
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1
ER -