Information Technology Reference
In-Depth Information
Objects as transformers
Imagine a scenario in which chorus members are represented as masks that in-
teractors adopt as they enter the computational domain. While not mandating
any particular comment, the representational quality of each mask could sug-
gest a view or mood to which the player would respond. The appearance of the
mask could change through successive enactments, as interactions accumulate.
For example, a player's speech prosody may affect the facial expression of the
mask (e.g., Cahn 1999). It may even cause a new digital mask to spin off as a
prop for future chorus members.
Interactions could register in various forms: for example, the system may
associate manual gestures or facial expressions with certain emotions (e.g., Pin-
hanez 1999; Wilson & Bobick 1999; Wren 1999). Objects could collect and
transmit information about who is using them, how, when, for how long, etc.
(e.g., Brave et al. 1998; Resnick 1998).
Multiple interactors could convene in a real-world “smart room” sort of
theatrical space, in a local-net multiuser virtual domain, or via a web-based
graphical environment. Each of these milieus implies a different scale in terms
of the number of simultaneous participants, and each scale would necessitate
particular strategies for coordinating the multiple inputs, but the basic modes
of interaction could apply to each.
Ultimately, the narrative system would reflect a plurality of influences. Its
choral aspect would become richer over time, as cumulative feedback formed a
collective construction, perhaps reminiscent of phenomena like the AIDS quilt.
Interestingly, this notion of multiple voices shaping the system also reflects
contemporary models of mind (Bakhtin 1981; Minsky 1986; Mondykowski
1982; Wertsch 1991).
Acknowledgments
Thanks to Marc Davis, Lise Motherwell, Warren Sack, and members of the Nar-
rative Intelligence reading group at the MIT Media Laboratory, as well as Adele
Goldberg, David Liebs, and others at Neometron, Inc., for helpful discussions.
Michael Mateas and Phoebe Sengers provided feedback that helped to improve
this paper.
