A Language-Based Model for Specifying and Staging Mixed-Initiative Dialogs

Specifying and implementing flexible human-computer dialogs, such as those used in kiosks, is complex because of the numerous and varied directions in which each user might steer a dialog. The objective of this research is to improve dialog specification and implementation. To do so we developed a model for specifying and staging mixed-initiative dialogs. The model involves a dialog authoring notation, based on concepts from programming languages, for specifying a variety of unsolicited reporting, mixed-initiative dialogs in a concise representation that serves as a design for dialog implementation. Guided by this foundation, we built a dialog staging engine which operationalizes dialogs specified in this notation. The model, notation, and engine help automate the engineering of mixed-initiative dialog systems. These results also provide a proof-of-concept for dialog specification and implementation from the perspective of theoretical programming languages. The ubiquity of dialogs in domains such as travel, education, and health care with the increased use of interactive voice-response systems and virtual environments provide a fertile landscape for further investigation of these results

Towards a crowdsourced solution for the authoring bottleneck in interactive narratives

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

Competition and Selection Among Conventions

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the online domain, where the data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions. Our analysis is based on the spread of low-level authoring conventions in the eprint arXiv over 24 years: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of them; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time--and at the individual level about the outcome of "fights" between people over convention choices.Comment: To appear in Proceedings of WWW 2017, data at https://github.com/CornellNLP/Macro