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Learning Plan Schemata from Observation: Explanation‐Based Learning for Plan Recognition
Author(s) -
Mooney Raymond J.
Publication year - 1990
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog1404_1
Subject(s) - schema (genetic algorithms) , computer science , plan (archaeology) , narrative , artificial intelligence , cognitive science , machine learning , psychology , linguistics , philosophy , archaeology , history
This article discusses how explanation‐based learning of plan schemata from observation can improve performance of plan recognition. The GENESIS program is presented as an implemented system for narrative text understanding that learns schemata and improves its performance. Learned schemata allow GENESIS to use schema‐based understanding techniques when interpreting events and thereby avoid the expensive search associated with plan‐based understanding. Learned schemata also function as new concepts that can be used to cluster examples and index events in memory. In addition, experiments are reviewed which demonstrate that human subjects, like GENESIS, con learn a schema by observing, explaining, and generalizing a single specific instance presented in a narrative.