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.
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A level designer typically creates the levels of a game to cater for a certain set of objectives, or mission. But in procedural content generation, it is common to treat the creation of missions and the generation of levels as two separate concerns. This often leads to generic levels that allow for various missions. However, this also creates a generic impression for the player, because the potential for synergy between the objectives and the level is not utilised. Following up on the mission-space generation concept, as described by Dormans, we explore the possibilities of procedurally generating a level from a designer-made mission. We use a generative grammar to transform a mission into a level in a mixed-initiative design setting. We provide two case studies, dungeon levels for a rogue-like game, and platformer levels for a metroidvania game. The generators differ in the way they use the mission to generate the space, but are created with the same tool for content generation based on model transformations. We discuss the differences between the two generation processes and compare it with a parameterized approach.
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This paper addresses the procedural generation of levels for collaborative puzzle-platform games. To address this issue, we distinguish types of multiplayer interaction, focusing on two-player collaboration, and identify relevant game mechanics for a puzzle-platform game, addressing player movement, interaction with moving game objects, and physical interaction involving both players. These are further formalized as game design patterns. To test the feasibility of the approach, a level generator has been implemented based on a rule-based approach, using the existing tool called Ludoscope and a prototype game developed in the Unity game engine. The level generation procedure results in over 3.7 million possible playable level variations that can be generated automatically. Each of these levels encourages or even requires both players to engage in collaborative gameplay.
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Grammar-based procedural level generation raises the productivity of level designers for games such as dungeon crawl and platform games. However, the improved productivity comes at cost of level quality assurance. Authoring, improving and maintaining grammars is difficult because it is hard to predict how each grammar rule impacts the overall level quality, and tool support is lacking. We propose a novel metric called Metric of Added Detail (MAD) that indicates if a rule adds or removes detail with respect to its phase in the transformation pipeline, and Specification Analysis Reporting (SAnR) for expressing level properties and analyzing how qualities evolve in level generation histories. We demonstrate MAD and SAnR using a prototype of a level generator called Ludoscope Lite. Our preliminary results show that problematic rules tend to break SAnR properties and that MAD intuitively raises flags. MAD and SAnR augment existing approaches, and can ultimately help designers make better levels and level generators.
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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.
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In this paper we present an experiment which has been performed to validate a pragmatic-based, expert-based and basic-level ontology. These ontologies were created for use in an application which generates questions for ordinary people with the purpose to determine a crisis situation. All three ontologies have specific characteristics related to their method of creation. This experiment shows that using the basic-level ontology results in the fastest and least ambiguous determination of a crisis situation.
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The propagandization of a Net Generation adds nothing to our understanding of the digital behaviour of young people. Indeed, it is becoming increasingly obvious that the whole concept of a Net Generation rests on incorrect assumptions. Hence, arguments based on a Net Generation are not only irrelevant and misleading but precarious as well. Precarious in the sense that they are mobilized as a decisive means of engineering change, not least in education policy. Only when we stop thinking in terms of the Net Generation can we form a more astute vision of when the deployment of digital learning aids will have a realistic chance of success.
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BACKGROUND: Today's nursing school applicants are considered “digital natives.” This study investigated students' views of new health care technologies. METHOD: In a cross-sectional survey among first-year nursing students, 23 common nursing activities and five telehealth nursing activities were presented along with three statements: “I consider this a core task of nursing,” “I look forward to becoming trained in this task,” and “I think I will do very well in performing this task.” RESULTS: Internet-generation nursing students (n = 1,113) reported a significantly (p ⩽ .001) less positive view of telehealth activities than of common nursing activities. Median differences were 0.7 (effect size [ES], −0.54), 0.4 (ES, −0.48), and 0.3 (ES, −0.39), measured on a 7-point scale. CONCLUSION: Internet-generation nursing students do not naturally have a positive view of technology-based health care provision. The results emphasize that adequate technology and telehealth education is still needed for nursing students. [J Nurs Educ. 2017;56(12):717–724.]
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When examining enrollment and graduation grades, higher education remains less accessible for first-generation students. Dutch first-generation students are also less likely to attend honors talent programs. However, not much is known what is driving these effects. First-generation honors students might face identity-related and psychological challenges, such as identity incompatibility, which is associated with low levels of sense of belonging and self-efficacy. This study investigates what identity-related psychological obstacles first-generation students experience in honors talent programs through three studies using a mixed-method approach. Results showed that psychological identity factors are obstacles for first-generation students in honors talent programs, though these obstacles vary over time. First-generation students in honors talent programs experience more identity incompatibility than their continuing-generation peers. However, identity incompatibility does not influence their reasons for not participating in honors programs; instead, (lack of) self-efficacy does. Moreover, quantitative data showed that higher levels of identity incompatibility before and during the honors talent program relate to lower levels of (anticipated) sense of belonging and self-efficacy. However, the qualitative part of the study showed that students generally report relatively high levels of sense of belonging and self-efficacy in the honors talent program. Together, these results show that even though the honors talent program can be a warm and welcoming safe space for first-generation students, there also is a need for honors educators in The Netherlands and abroad to become more aware of the struggles of first-generation students and actively invest in recruiting and supporting this group of students in honors talent programs.
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With this paper, it is illustrated that a focus on entrepreneurship training in the nature and wilderness sector is relevant for diverse organisations and situations. The first curricula on nature entrepreneurship are currently being developed. In this paper the authors describe a project that focusses on educating the next generation of nature entrepreneurs, reflect on the Erasmus Intensive Program ‘European Wilderness Entrepreneur’ and the Wild10 World Café on nature entrepreneurship training. Sharing and learning from experiences is highly recommended to further develop and strengthen the curricula while considering the dynamic context of nature conservation and restoration of ecological processes.
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