We present a declarative, generative model of simple narratives. Our model contributes to automation of natural language processing because it provides a model of the relationship between surface text and its meaning. This work surpasses previous story modeling work in three important ways. First, we build on developments in commonsense knowledge representation to construct precise formal definitions of components of simple narratives and relations among these components. Second, we develop a new theory of rational intention in autonomous agents to formalize the relation of a character's mental state to actions it undertakes in a narrative. Third, we implement this model in the first story generation system to use a formal grammar for stories The centerpiece of our model is a story grammar expressing characteristics of event sequences which, when reported in natural language, constitute a narrative. This grammar draws on a separate theory of rational intention in autonomous agents. The grammar makes use of interchangeable world models specifying characters that may appear in a story, emotions they feel, actions they take, and events that happen in narratives We implement our model in a story generation system which simultaneously generates text and meaning representations. This system represents a significant achievement since it is the first such system constructed from an explicit, formal model for stories. The implementation serves as a concrete demonstration of the computational viability of our model. By generating both semantic representations and the corresponding surface text, our system provides a criterion of correct understanding since surface texts are paired with corresponding internal representations Our theory of rational intention supports reasoning about the successes and failures of intentional actions and the ramifications of these outcomes. Also, this theory captures relative goals, deadline goals, and recurring goals. Previous theories of intention did not adequately handle as broad a class of goals. Finally, our theory of intention supports the conclusion that an agent which adopts a goal will commit to actions in pursuit of that goal. In addition to its application to narrative understanding, our theory of rational intention is applicable to multi-agent planning and speech-act processing