Proceedings of the Annual Meeting of the Cognitive Science Society

We describe a system that learns new schemas by modifying old ones, in order to understand anomalous events in stories that it reads. W e discuss how these schemas (called Explanation Patterns[Schank 86]) are structured in order to make them modifiable, and how the understanding processapplies and modifies them. This model bridges the gap between two previous models of understanding,which were based on either application of prestored schemas or understanding-time inference chaining.By employing modifiable schemzis, our model is more flexible than the former and more efficient thanthe latter.