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.

Figuring out what plan another agent might be executing is an important and difficult type of explanation problem which involves a special type of knowledge about plans and goals. Elsewhere, we have discussed an approach to the general explanation problem that involves adapting stored explanations to new situations. In this paper, we review that discussion very briefly and then examine the special issues that arise when applying that approach to explaining intentional actions, focusing on adaptation strategies that are specifically relevant to plan recognition.

In this paper we argue that the key to teaching someone to perform a complex task is to interleave instruction and practice in a way that exploits the synergism between the two effectively. Furthermore, w e argue that computer simulations provide a particularly promising environment in which to achieve this interleaving. We will illustrate our argument by describing a simulation-based system we are building to train people to perform complex social tasks, such as selling consulting services. In particular, we will focus on the system's ability to present real-world cases at the moment that they are relevant to the student's simulated activities. In doing so, we hope to contribute both to the construction of useful teaching system and to the theory of case-based reasoning, particularly in case retrieval.