Understanding speech in interactive narratives with crowd sourced data

Speech recognition failures and limited vocabulary coverage pose challenges for speech interactions with characters in games. We describe an end-to-end system for automating characters from a large corpus of recorded human game logs, and demonstrate that inferring utterance meaning through a combination of plan recognition and surface texts similarity compensates for recognition and understanding failures significantly better than relying on surface similarity alone.Singapore-MIT GAMBIT Game La

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

The Sight and Site of North Korea: Citizen Cartography\u27s Rhetoric of Resolution in the Satellite Imagery of Labor Camps

In recent years, satellite mapping of North Korea, especially of its labor camps, has become important forms of evidence of human rights violations, used by transnational advocacy groups to lobby to Western governments for change. A phenomenon of “citizen cartography” has emerged where non-expert humanitarian actors use commercially available software like Google Earth to “infiltrate” the borders of North Korea. This essay interrogates the politics of seeing that takes place in creating the site and sight of North Korea by citizen cartographers, and historicizes these processes of seeing in Cold War and post-Cold War visual culture. Specifically, citizen cartography of North Korea engages in rhetorics of resolution, where the cartographer continually searches for a better, clearer view of the ground below, while still constrained by corporate software and logics of state sovereignty that make it difficult to resolve the problem of forced labor

Simulated role-playing from crowdsourced data

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 173-178).Collective Artificial Intelligence (CAl) simulates human intelligence from data contributed by many humans, mined for inter-related patterns. This thesis applies CAI to social role-playing, introducing an end-to-end process for compositing recorded performances from thousands of humans, and simulating open-ended interaction from this data. The CAI process combines crowdsourcing, pattern discovery, and case-based planning. Content creation is crowdsourced by recording role-players online. Browser-based tools allow nonexperts to annotate data, organizing content into a hierarchical narrative structure. Patterns discovered from data power a novel system combining plan recognition with case-based planning. The combination of this process and structure produces a new medium, which exploits a massive corpus to realize characters who interact and converse with humans. This medium enables new experiences in videogames, and new classes of training simulations, therapeutic applications, and social robots. While advances in graphics support incredible freedom to interact physically in simulations, current approaches to development restrict simulated social interaction to hand-crafted branches that do not scale to the thousands of possible patterns of actions and utterances observed in actual human interaction. There is a tension between freedom and system comprehension due to two bottlenecks, making open-ended social interaction a challenge. First is the authorial effort entailed to cover all possible inputs. Second, like other cognitive processes, imagination is a bounded resource. Any individual author only has so much imagination. The convergence of advances in connectivity, storage, and processing power is bringing people together in ways never before possible, amplifying the imagination of individuals by harnessing the creativity and productivity of the crowd, revolutionizing how we create media, and what media we can create. By embracing data-driven approaches, and capitalizing on the creativity of the crowd, authoring bottlenecks can be overcome, taking a step toward realizing a medium that robustly supports player choice. Doing so requires rethinking both technology and division of labor in media production. As a proof of concept, a CAI system has been evaluated by recording over 10,000 performances in The Restaurant Game, automating an Al-controlled waitress who interacts in the world, and converses with a human via text or speech. Quantitative results demonstrate how CAI supports significantly more open-ended interaction with humans, while focus groups reveal factors for improving engagement.by Jeffrey David Orkin.Ph.D