This paper describes our current approach in implementing computational improvisational micro-agents. This approach is intended to foster bottom-up research to better understand how to build more complex agent behaviors in a theatrical improvisational setting. Micro-agent designs are based on our current findings in a multi-year study focused on studying real life theatrical improvisers with an aim towards better understanding the cognition employed inimprovisation at the individual and group level. It also introduces a key architectural component from the domain of fuzzy logic that enables us to clearly represent some of our current findings.

A key challenge in computational narrative is story generation. In this paper we focus on analogy-based story generation, and, specifically, on how to generate both story and text using analogy. We present a dual representation formalism where a human-understandable representation (composed of English sentences) and a computer-understandable representation (consisting in a graph) are linked together in order to generate both story and natural language text by analogy. We have implemented our technique in the Riu interactive narrative system.

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.

The Pataphysic Institute (PI) is a research prototype multi-player game world. In PI, the personalities of the inhabitants are the base for the game mechanics. When interacting with other characters the potential emotional reactions depend upon avatars' current mood and personality. PI is built with inspiration from personality psychology and affect theory in an attempt to mimic possible emotional responses in order to give the player support in role-playing. The mental states of characters depend on their personalities and on their current moods. Moods differ according to context and to recent experiences. Emotional experiences become memories and define the relationships between characters. The mental state is the sum of the character and governs what actions can be performed in a given moment. In order to do certain things the characters need to be in certain moods — and for this the players need to game their avatars' emotions, and game their relationships.

In the context of interactive, virtual experiences, the use of personality models to maintain consistent character behaviour is becoming more widespread in both industry and academia. Most current techniques, however, are limited in one of three ways: either they overly restrict user actions, have a high cost for creating varied content, or rely on a representation that prohibits conveying complex content to the user.  Toward addressing these issues, we introduce Socially Consistent Role Passing, a mechanism for ensuring consistent character behaviour that leverages the design of PaSSAGE, an existing system for generating adaptive, interactive stories.  While results from previous human user studies have shown that PaSSAGE improves the enjoyment of players with little gaming experience, we present results from a new study showing that PaSSAGE's adaptive stories, augmented with Socially Consistent Role Passing, improve the enjoyment of all players versus a set of fixed-structure alternatives.

The first major story generation system, which preceded Minstrel and which While ongoing progress in digital entertainment also received significant attention, is Tale-Spin (Meehan technology continues, commercial designers still largely 1977). Like Minstrel, this system generates stories which eschew systems for procedural story generation, preferring satisfy user-submitted requirements. Tale-Spin creates instead to generate content by hand. In the academic English stories by planning a method for the main literature, projects such as (Appling & Riedl 2009, Roberts character to achieve her or his goal, using inferences and & Isbell 2009) continue to investigate ways to improve the rules to generate a large number of details about a story nuances of interactive storytelling while others attempt to (many of which do little contribute to an audience create their own systems to investigate ways to use experience). This contrasts nicely with Minstrel, which knowledge from interactive narrative and story generation performs no logical inferences and which performs all in new fields such as playable games (Drachen & Hitchens actions from the point of view of an author, manipulating et al. 2009, Sullivan, Mateas & Wardrip-Fruin 2009).

This research aims at building a preference-based player model of Civilization IV players. Our model incorporates attributes which are defined for AI players. We use a sequential minimal optimization (SMO) classifier to build the player model based on a training set with observations of a large number of games between six AI players. The model was validated on a test set of games between the same six AI players. While it did not seem to generalize well to the preferences of different AI players, it did manage to accurately predict some of the preferences for a veteran human player. Further tests showed that AI players with the same play styles but different preference values were often confused by the model. We conclude that for a complex game such as Civilization IV a model that attempts to accurately predict specific preference values is hard to construct. A model that focusses on play styles might succeed better.

Story Canvas is a visual authoring tool for the creation of interactive, generative stories. Aimed at authors without a technical background in computational storytelling, our system takes an existing author goal-based narrative planning architecture and adds a highly visual authoring and reading interface to the technology, using the language of storyboards and comics as a framework for both authoring and interacting with the resulting narratives. In this paper we describe Story Canvas and its evolution from our previous authoring work, including how our interface choices have been driven by our previous experiences with non-technical authors, and describe the details of translating the visual authoring constructs into story plans within the story generator.

In order to cooperate effectively with human players, characters need to infer the tasks players are pursuing and select contextually appropriate responses. This process of parsing a serial input stream of observations to infer a hierarchical task structure is much like the process of compiling source code. We draw an analogy between compiling source code and compiling behavior, and propose modeling the cognitive system of a character as a compiler, which tokenizes observations and infers a hierarchical task structure. An evaluation comparing automatically compiled behavior to human annotation demonstrates the potential for this approach to enable AI characters to understand the behavior and infer the tasks of human partners.

Team coordination of non-player characters can create a deeper sense of immersion in real-time games by allowing characters to work together to produce better tactics and strategy. Achieving multi-agent coordination can be a difficult problem, and can incur substantial computational costs. Our goal with this work is to produce a reactive method for coordinating game characters that will allow computationally inexpensive team coordination. Reactive teaming creates teams of agents through the use of simple constant-time agent interactions without increasing the difficulty of authoring game characters.