Seminar on Computational Learning and
Adaptation
Learning Hierarchical Skills from Observation
Ryutaro Ichise
National Institute of Informatics, Japan
Center for the Study of Language and Information, Stanford University
ichise@nii.ac.jp
This presentation addresses the problem of learning control skills from
observation. In particular, we show how to infer a hierarchical, reactive program that reproduces and explains the observed actions of other agents, specifically the elements that are shared across multiple individuals. We infer these programs using a three-stage process that learns flat unordered rules, combines these rules into a classification hierarchy, and finally translates this structure into a hierarchical reactive program. The resulting program is concise and easy to understand, making it possible to view program induction as a practical technique for knowledge acquisition.
This talk describes joint work with Daniel Shapiro and Pat Langley.
Date: Thursday, May 16
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Time: 4:15-5:30PM
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Place: Cordura 100
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