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

A Multi-Dimensional Approach for Framing Crowdsourcing Archetypes

All different kinds of organizations – business, public, and non-governmental alike – are becoming aware of a soaring complexity in problem solving, decision making and idea development. In a multitude of circumstances, multidisciplinary teams, high-caliber skilled resources and world-class computer suites do not suffice to cope with such a complexity: in fact, a further need concerns the sharing and ‘externalization’ of tacit knowledge already existing in the society. In this direction, participatory tendencies flourishing in the interconnected society in which we live today lead ‘collective intelligence’ to emerge as key ingredient of distributed problem solving systems going well beyond the traditional boundaries of organizations. Resulting outputs can remarkably enrich decision processes and creative processes carried out by indoor experts, allowing organizations to reap benefits in terms of opportunity, time and cost. Taking stock of the mare magnum of promising opportunities to be tapped, of the inherent diversity lying among them, and of the enormous success of some initiative launched hitherto, the thesis aspires to provide a sound basis for the clear comprehension and systematic exploitation of crowdsourcing. After a thorough literature review, the thesis explores new ways for formalizing crowdsourcing models with the aim of distilling a brand-new multi-dimensional framework to categorize various crowdsourcing archetypes. To say it in a nutshell, the proposed framework combines two dimensions (i.e., motivations to participate and organization of external solvers) in order to portray six archetypes. Among the numerous significant elements of novelty brought by this framework, the prominent one is the ‘holistic’ approach that combines both profit and non-profit, trying to put private and public sectors under a common roof in order to examine in a whole corpus the multi-faceted mechanisms for mobilizing and harnessing competence and expertise which are distributed among the crowd. Looking at how the crowd may be turned into value to be internalized by organizations, the thesis examines crowdsourcing practices in the public as well in the private sector. Regarding the former, the investigation leverages the experience into the PADGETS project through action research – drawing on theoretical studies as well as on intensive fieldwork activities – to systematize how crowdsourcing can be fruitfully incorporated into the policy lifecycle. Concerning the private realm, a cohort of real cases in the limelight is examined – having recourse to case study methodology – to formalize different ways through which crowdsourcing becomes a business model game-changer. Finally, the two perspectives (i.e., public and private) are coalesced into an integrated view acting as a backdrop for proposing next-generation governance model massively hinged on crowdsourcing. In fact, drawing on archetypes schematized, the thesis depicts a potential paradigm that government may embrace in the coming future to tap the potential of collective intelligence, thus maximizing the utilization of a resource that today seems certainly underexploited

Swift trust and behavioral change: facilitating factors of crowdsourcing in chronic disease prevention

Behind Internet usage habits there is a common vocabulary: trust. In order to promote preventive medicine, Internet medical care has been trying to cultivate user habits and behavior change, but whoever increases trust can go further. The Internet has accelerated the pace of work and life and generalized the temporary involvement of individuals and teams. In many organizations, there is usually no time to develop trust among team members or between the team and customers in traditional ways such as mutual familiarity, experience sharing, mutual disclosure, and verification of commitments. These new situations have led to the study of a new form of trust: "swift trust". According to Hurd et al. (2017), "swift trust" focuses on expecting that a person has the necessary attributes to be relied upon. In the "swift trust" theory, a group or individual assumes the existence of trust initially, and later verifies and adjusts trust beliefs accordingly. Faced with the problem of the rapid spread of chronic diseases and the high proportion of medical expenses needed to combat them and that have posed challenges to the national finances in China, this thesis focuses on studying the factors that may facilitate the establishment of "swift trust" in the Internet based chronic disease crowdsourcing model. Grounded on the idea that trust affects behavior and speed affects efficiency, we have reviewed extant literature and, with the help of ROST Content Mining (ROST-CM) text mining software, we dug millions of Internet data and conducted in-depth research on the "swift trust" problem. Results, later verified through two ongoing healthcare projects showed that "profession" followed by "platform", "dissemination" and "propensity" are the most critical factors that affect the establishment of swift trust. These results may be of interest to professionals, organizations and government decision makers in need of establishing and winning trust, and particularly "swift trust", as an essential ingredient in the sharing economy.Existe uma palavra comum por detrás de todos os hábitos de utilização da Internet: confiança. Com o objetivo de promover a medicina preventiva, alguns cuidados médicos prestados através da Internet têm vindo a procurar motivar os utilizadores para uma mudança de hábitos e comportamentos, mas apenas quem conseguir ganhar a confiança poderá ir mais longe. A Internet acelerou o ritmo da vida e do trabalho e generalizou a participação temporária de indivíduos e grupos. Em muitas organizações, não há tempo suficiente para se criar confiança entre os membros de um grupo ou entre grupos e indivíduos através de formas tradicionais como a convivência e o conhecimento mútuos, a partilha de experiências ou a verificação do cumprimento de compromissos. Esta situação levou ao estudo de uma nova forma de confiança: "a confiança imediata". Hurd et al. (2017) afirmam que este conceito se refere à expetativa de que uma determinada pessoa reúna os atributos necessários para ser confiável. Segundo a teoria que estuda a "confiança imediata", um grupo ou indivíduo assume desde logo a presença de confiança e reserva para mais tarde a confirmação da sua existência. Considerando os desafios colocados pelo rápido desenvolvimento de doenças crónicas num país tão populoso como a China e a necessidade de as combater, esta tese estuda os fatores que poderão facilitar a construção de "confiança imediata" no modelo de colaboração aberta através da Internet com vista à prevenção destas doenças. Partindo do princípio de que a confiança afeta os comportamentos e de que a rapidez afeta a eficiência procedeu-se à revisão de literatura sobre o tema e, com a ajuda do "software" de mineração de texto ROST-CM (ROST Content Mining) foram recolhidos e tratados milhões de dados extraídos da Internet. Os resultados foram depois confrontados com a prática de dois projetos na área da saúde e revelaram que a "profissão" seguida da "plataforma", "disseminação" e "propensão" são os fatores que mais contribuem para a formação de "confiança imediata". Os resultados obtidos poderão ser de interesse para profissionais, organizações e decisores governamentais que necessitam de construir e manter confiança e, em particular "confiança imediata", enquanto ingrediente essencial na economia de partilha

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl