Interactive Storytelling research has produced a wealth of technologies that can be
employed to create personalised narrative experiences, in which the audience takes
a participating rather than observing role. But so far this technology has not led
to the production of large scale playable interactive story experiences that realise
the ambitions of the field. One main reason for this state of affairs is the difficulty
of authoring interactive stories, a task that requires describing a huge amount of
story building blocks in a machine friendly fashion. This is not only technically
and conceptually more challenging than traditional narrative authoring but also a
scalability problem.
This thesis examines the authoring bottleneck through a case study and a literature
survey and advocates a solution based on crowdsourcing. Prior work has already
shown that combining a large number of example stories collected from crowd workers
with a system that merges these contributions into a single interactive story can be
an effective way to reduce the authorial burden. As a refinement of such an approach,
this thesis introduces the novel concept of Crowd Task Adaptation. It argues that in
order to maximise the usefulness of the collected stories, a system should dynamically
and intelligently analyse the corpus of collected stories and based on this analysis
modify the tasks handed out to crowd workers.
Two authoring systems, ENIGMA and CROSCAT, which show two radically different
approaches of using the Crowd Task Adaptation paradigm have been implemented and
are described in this thesis. While ENIGMA adapts tasks through a realtime dialog
between crowd workers and the system that is based on what has been learned from
previously collected stories, CROSCAT modifies the backstory given to crowd workers
in order to optimise the distribution of branching points in the tree structure that
combines all collected stories. Two experimental studies of crowdsourced authoring
are also presented. They lead to guidelines on how to employ crowdsourced authoring
effectively, but more importantly the results of one of the studies demonstrate the
effectiveness of the Crowd Task Adaptation approach
