This article presents a novel and highly interactive process to generate natural language narratives based on our ongoing work on semiotic relations, providing four criteria for composing new narratives from existing stories. The wide applicability of this semiotic reconstruction process is suggested by a reputed literary scholar's deconstructive claim that new narratives can often be shown to be a tissue of previous narratives. Along, respectively, three semiotic axes – syntagmatic, paradigmatic, and meronymic – existing stories can yield new stories by the combination, imitation, or expansion of an iconic scene; lastly, a new story may emerge through reversal via an antithetic consideration, i.e., through the adoption of opposite values. Targeting casual users, we present a fully operational prototype with a simple and user-friendly interface that incorporates an AI agent, namely ChatGPT. The prototype, in a coauthor capacity, generates context-compatible sequences of events in storyboard format using backward-chaining abductive reasoning (employing Stable Diffusion to draw scene illustrations), conforming as much as possible to the user's authorial instructions. The extensive repertoire of book and movie summaries available to the AI agent obviates the need to manually supply laborious and error-prone context specifications. A user study was conducted to evaluate user experience and satisfaction with the generated narratives. The preliminary findings suggest that our approach has the potential to enhance story quality while offering a positive user experience.
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This book is the account of teaching practice linked to research projects, a practice that is able to create new, unexpected values in the complex patchwork of the city through experimental and strategic interventions with greenery. That the interventions involve greenery is obviously linked to the fact that the Van Hall Larenstein university of applied sciences specializes in nature and agriculture, but there is also a practical reason. Green spaces act as a cohesive force, as is shown again and again in the Netherlands and in the Lively Cities programme. Particularly in the urban context, green spaces have a distinctive and perhaps even emotional value that encourages people to pause there and makes them think about their appreciation of a place. Greenery triggers people to take part in social experiments. But that is just the beginning.
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Augmented Play Spaces (APS) are (semi-) public environments where playful interaction isfacilitated by enriching the existing environment with interactive technology. APS canpotentially facilitate social interaction and physical activity in (semi-)public environments. Incontrolled settings APS show promising effects. However, people’s willingness to engagewith APSin situ, depends on many factors that do not occur in aforementioned controlledsettings (where participation is obvious). To be able to achieve and demonstrate thepositive effects of APS when implemented in (semi-)public environments, it is important togain more insight in how to motivate people to engage with them and better understandwhen and how those decisions can be influenced by certain (design) factors. TheParticipant Journey Map (PJM) was developed following multiple iterations. First,based on related work, and insights gained from previously developed andimplemented APS, a concept of the PJM was developed. Next, to validate and refinethe PJM, interviews with 6 experts with extensive experience with developing andimplementing APS were conducted. Thefirst part of these interviews focused oninfluential (design) factors for engaging people into APS. In the second part, expertswere asked to provide feedback on thefirst concept of the PJM. Based on the insightsfrom the expert interviews, the PJM was adjusted and refined. The Participant JourneyMap consists of four layers: Phases, States, Transitions and Influential Factors. There aretwo overarchingphases:‘Onboarding’and‘Participation’and 6statesa (potential)participant goes through when engaging with an APS:‘Transit,’‘Awareness,’‘Interest,’‘Intention,’‘Participation,’‘Finishing.’Transitionsindicate movements between states.Influential factorsare the factors that influence these transitions. The PJM supportsdirections for further research and the design and implementation of APS. Itcontributes to previous work by providing a detailed overview of a participant journeyand the factors that influence motivation to engage with APS. Notable additions are thedetailed overview of influential factors, the introduction of the states‘Awareness,’‘Intention’and‘Finishing’and the non-linear approach. This will support taking intoaccount these often overlooked, key moments in future APS research and designprojects. Additionally, suggestions for future research into the design of APS are given.
