This paper examines the phenomenon of daydreaming: spontaneously recalling or imagining personal or vicarious experiences in the past or future. The following important roles of daydreaming in human cognition are postulated: plan preparation and rehearsal, learning from failures and successes, support for processes of creativity, emotion regulation, and motivation. A computational theory of daydreaming and its implementation as the program DAYDREAMER are presented. DAYDREAMER consists of 1) a scenario generator based on relaxed planning, 2) a dynamic episodic memory of experiences used by the scenario generator, 3) a collection of personal goals and control goals which guide the scenario generator, 4) an emotion component in which daydreams initiate, and are initiated by, emotional states arising from goal outcomes, and 5) domain knowledge of interpersonal relations and common everyday occurrences. The role of emotions and control goals in daydreaming is discussed. Four control goals commonly used in guiding daydreaming are presented: rationalization, failure/success reversal, revenge, and preparation. The role of episodic memory in daydreaming is considered, including how daydreamed information is incorporated into memory and later used. An initial version of DAYDREAMER which produces several daydreams (in English) is currently running.

This thesis describes new approaches to the formal modeling of narrative discourse. Although narratives of all kinds are ubiquitous in daily life, contemporary text processing techniques typically do not leverage the aspects that separate narrative from expository discourse. We describe two approaches to the problem. The first approach considers the conversational networks to be found in literary fiction as a key aspect of discourse coherence; by isolating and analyzing these networks, we are able to comment on longstanding literary theories. The second approach proposes a new set of discourse relations that are specific to narrative. By focusing on certain key aspects, such as agentive characters, goals, plans, beliefs, and time, these relations represent a theory-of-mind interpretation of a text. We show that these discourse relations are expressive, formal, robust, and through the use of a software system, amenable to corpus collection projects through the use of trained annotators. We have procured and released a collection of over 100 encodings, covering a set of fables as well as longer texts including literary fiction and epic poetry. We are able to inferentially find similarities and analogies between encoded stories based on the proposed relations, and an evaluation of this technique shows that human raters prefer such