July 20, 2005

New Dissertations on AI-Based Interactive Art, Character and Narrative

by Andrew Stern · , 5:28 pm

I thought I’d link to a few dissertations that have been published recently that may be of interest to GTxA readers. Perfect for a summer read on the beach with your laptop, right? Here are excerpts from their abstracts.

Autonomous Expressionism and Network Arts: New Paradigms in Art, Emotional Interaction, and Information Retrieval, by David Ayman Shamma, Northwestern University
In this dissertation, I depart from traditional computer science metrics and methodologies and introduce a new framework for building computer systems with an emphasis on the creation of new artistic installations and interactions. Specifically, I introduce Autonomous Expressionism, an extension of Abstract Expressionism, whose goal optimizes the emotional experience in human computer interaction.

Narrative Planning: Balancing Plot and Character (pdf), by Mark Riedl, North Carolina State University
In this dissertation, I explore the use of search-based planning as a technique for generating stories that demonstrate both strong plot coherence and strong character believability. First, I describe an extension to search-based planning that reasons about character intentions by identifying possible character goals that explain their actions in a plan and creates plan structure that explains why those characters commit to their goals. Second, I describe how a character personality model can be incorporated into planning in a way that guides the planner to choose consistent character behavior without strictly preventing characters from acting “out of character” when necessary. Finally, I present an open-world planning algorithm that extends the capabilities of conventional planning algorithms in order to support a process of story creation modeled after the process of dramatic authoring used by human authors.

Hierarchical Parallel Markov Models for Interactive Social Agents (pdf), by Robert Zubek, Northwestern University
In this report I present hierarchical parallel Markov models for the creation of interactive social agents for video games and entertainment. The approach extends existing, popular character technologies for social, communicative interaction. First, adding the knowledge of temporal interaction structure enables natural language interaction on a time scale much longer than current chatterbot technologies. Second, adding support for hierarchical interaction decomposition, where an interaction is represented as a collection of smaller, simpler elements, simplifies the authoring of complex engagement. Third, adding support for the concurrent engagement of these elements enables engagement in interleaved, naturalistic communication. The resulting decomposition supports redundancy of representation, graceful performance degradation through the simultaneous engagement of behaviors on different levels of abstraction, and the stochastic approximation mechanism increases robustness in the face of noise and ambiguity.

Story Games and the OPIATE System, by Chris R. Fairclough, University of Dublin – Trinity College
This thesis presents a new approach to creating game mechanics, utilizing a number of key concepts that result in an interaction scheme that engages a player with a story, while allowing the player the freedom to interact with and alter that story as it happens. A story director agent was developed that uses case-based planning of skeletal plot scripts, modelled on Propp’s morphology, and the dynamic adaptation of these plans. This agent was incorporated into a social simulation engine that a player interacts with through controlling one of the characters therein. The story director and social simulation are symbiotically linked, with a feedback mechanism that ensures plots are planned consistently with the simulation.