ConceptThe goal of the worksop/tutorial is to introduce participants to the fundamentals of Procedural Content Generation (PCG) based on generative grammars, have them experience an example of such a system first-hand, and discuss the potential of this approach for various areas of procedural content generation for games. The principles and examples are based on Ludoscope, a software tool developed at the HvA by Dr. Joris Dormans, e.a.Duration: 2 hoursOverviewWe will use the first 30 minutes to explain the basics of how to use generative grammars to generate levels. The principles of these grammars and model transformations will be demonstrated by means of the level generation system of Spelunky, which we have modeled in Ludoscope.Spelunky focuses solely on the generation of geometry, but grammar-based systems can also be used to transform more abstract concepts of level design into level geometry. In the next hour, the participants will be able to get some hands-on experience with Ludoscope. The assignment will be to generate a Mario-like level based on specific requirements, adapted to the interests of workshop participants.Finally, we are interested in the participants’ evaluation of this approach to PCG. We will use the last 20 minutes to discuss alternative techniques, and possible applications to other areas of PCG, like asset creation, scripting and game generation.Workshop participants are asked to bring a (PC) laptop to work on during the workshop, and are encouraged to work in pairs.
LINK
Developmental Transformations (DvT), a practice involving interactive, improvisational play in pairs or groups, has gained international appeal as a therapeutic intervention for different populations in a variety of health, care and recreational contexts. However, a rigorous review of the benefits of DvT has not been conducted. The purpose of this study was to review extant literature for the observed benefits of DvT, identify gaps in the literature and make recommendations concerning future research including identifying possible areas for outcome measurement for preliminary studies. The authors, who each completed training in this approach, conducted a scoping review of English-language, published, peer-reviewed and grey DvT literature through 2021. From an initial 745 records retrieved through databases and a manual search, 51 publications met criteria, which, when analysed using in-vivo and pattern coding, resulted in a total of seventeen categories of observed benefits ascribed to DvT. These included six general categories – relational, emotional, social, cognitive, behavioural and physical benefits – and eleven complex categories of benefits to participants across the lifespan. In addition to benefits for participants, benefits of DvT were also observed and reported for facilitators, therapists, teachers and supervisors engaged in this practice. This review revealed inconsistencies regarding the reporting of practitioner training, frequency, format, population, intended goals, assessment measures and outcomes. Future studies with increased experimental rigor, standardized outcome measures and consistent reporting are recommended.
This paper frames the process of designing a level in a game as a series of model transformations. The transformations correspond to the application of particular design principles, such as the use of locks and keys to transform a linear mission into a branching space. It shows that by using rewrite systems, these transformations can be formalized and automated. The resulting automated process is highly controllable: it is a perfect match for a mixed-initiative approach to level generation where human and computer collaborate in designing levels. An experimental prototype that implements these ideas is presented.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
The scientific publishing industry is rapidly transitioning towards information analytics. This shift is disproportionately benefiting large companies. These can afford to deploy digital technologies like knowledge graphs that can index their contents and create advanced search engines. Small and medium publishing enterprises, instead, often lack the resources to fully embrace such digital transformations. This divide is acutely felt in the arts, humanities and social sciences. Scholars from these disciplines are largely unable to benefit from modern scientific search engines, because their publishing ecosystem is made of many specialized businesses which cannot, individually, develop comparable services. We propose to start bridging this gap by democratizing access to knowledge graphs – the technology underpinning modern scientific search engines – for small and medium publishers in the arts, humanities and social sciences. Their contents, largely made of books, already contain rich, structured information – such as references and indexes – which can be automatically mined and interlinked. We plan to develop a framework for extracting structured information and create knowledge graphs from it. We will as much as possible consolidate existing proven technologies into a single codebase, instead of reinventing the wheel. Our consortium is a collaboration of researchers in scientific information mining, Odoma, an AI consulting company, and the publisher Brill, sharing its data and expertise. Brill will be able to immediately put to use the project results to improve its internal processes and services. Furthermore, our results will be published in open source with a commercial-friendly license, in order to foster the adoption and future development of the framework by other publishers. Ultimately, our proposal is an example of industry innovation where, instead of scaling-up, we scale wide by creating a common resource which many small players can then use and expand upon.
