We present a system for learning generalized, stereotypical patterns of events---or schemas''---from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a head start'' set of protoschemas--- schemas that a 1- or 2-year-old child would likely know---we can obtain useful, general world knowledge with very few story examples---often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.