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This paper addresses the problem of efficiently learning action schemas using a bounded number of samples (Interactions with the environment). The authors consider schemas in two languages - traditional STRIPS, and a new language STRIPS+WS that extends STRIPS to allow for the creation of new objects when an action is executed. This modification allows STRIPS+WS to model web services and can be used to describe web-service composition (Planning) problems. This paper shows that general STRIPS operators cannot be efficiently learned through raw experience, though restricting the size of action preconditions yields a positive result.
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