Fig.1 The AA Social Skill Training System
Our approach begins with a paradigm shift that re-conceptualizes social skill simulation as rehearsing and improvising roles instead of performing a role. We have adapted Stanislavsky's Active Analysis (AA) rehearsal technique as the design basis for social simulation training. AA was developed to help theater actors rehearse a script or text. The overall script is divided into key events (i.e., short scenes) that actors rehearse and improvise under a director's guidance. AA has two attributes especially relevant to social skills training. First, AA is designed to foster an actor's conceptualization of the beliefs, motivations and behavior of their own as well as other actors, and thus is developed to engender ToM reasoning. Second, by adopting AA to simulation based social skills training, the emphasis shifts to developing short scenes that allow variability and re-playability. Decomposition into short rehearsal scenes helps: (a) break the combinatorial explosion that exacerbates content creation for long narrative arcs, (b) support users replaying scenes, possibly in different roles with different virtual actors, and (c) users to directly experience in subsequent scenes the larger social consequences of behaviors.
AA provides a basis for experience design that uses ToM constructs and enables crowd sourcing to generate content for rich, coherent interactive experiences. Several researchers have proposed crowd sourcing techniques for narrative creation. The work presented here differs in that we focus on an iterative crowd sourcing method designed to create content for crafting a space of rich social interactions in which players explore a wide range of social gambits, from ethical persuasion and personal appeals to even deception; the content is created through the crowd using carefully designed tasks and interfaces that use AA and ToM as theoretical foundation.
We collected 108 stories using the same AA interface. The average length of a story was 6 lines. The stories collected show an impressive range in complexity, richness and variety in characters' actions.
Funding for this research was provided by the National Science Foundation Cyber-Human Systems under Grant No. 1526275.