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.
['Raymond J. Mooney']
Learning Plan Schemata from Observation: Explanation‐Based Learning for Plan Recognition