In 2D illustration an artist makes a series of decisions throughout the process, with those choices becoming more granular over time. These decisions vary in scope and impact. Given this formulation of a creative process, I observe that there exist portions of common workflows where an artist is largely following through on the logical conclusions of prior decisions. These are the specific areas of these creative workflows where I believe that co-creative tools can be implemented while retaining creative control over the completed artifact. My work so far has been in implementing such tools for steps in illustration and frame-by-frame animation. I intend to design further process-centered co-creative systems and study their usability and impact in the context of existing workflows.
Narrative planning can be used to create structured interactive experiences that dynamically respond to user input. Narrative planning works by generating a sequence of actions that achieves an author's desired goal while ensuring that there is an explanation for why each character takes each action in the sequence. An action in a sequence is considered necessary to that sequence if leaving the action out would prevent a later action in the sequence from being taken or prevent an author or character goal from being achieved. Using this definition, we define the causal width of a sequence to be the number of causally unnecessary actions, and we hypothesize sequences with a lower causal width are more likely to lead to a solution. We show that using causal width as a ranking mechanism can sometimes improve blind search, and ignoring stories with a high causal width can always improve the performance of heuristic search on a set of story benchmark problems.
We formalize the sensemaking needed to play information storygames as a task grounded in automated plan recognition, and outline extensions to a baseline model thereof needed to account for a key part of the information storygame-play loop: non-linear discovery of ambiguous information. This novel problem setting is non-trivial—mechanically simulating this narrative sensemaking requires piecing together incompletely-specified events to reason about the means through which particular ends were achieved in the virtual world. Our work readies plan recognition systems for the task by extending a foundational compilation of plan recognition as planning to cover partially-ordered and lifted observations of both actions and facts. While state-of-the-art plan recognizers approximate piecemeal aspects of these features, they do so in isolation of each other and by appealing to disparate and complex frameworks. In contrast, we achieve these functions within a classical planning-based framework. Our results confirm that, while slower, our approach never has more (and often has fewer) false positives than the baseline model in predicting the ground truth plan being executed. We discuss our findings in the context of future work toward better simulating human information storygame sensemaking.
