Procedural Content Generation Using Patterns as Objectives

Dahlskog, Steve; Togelius, Julian

doi:10.1007/978-3-662-45523-4_27

Cited by 17 publications

(13 citation statements)

References 9 publications

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“…Another approach that uses pattern and Objectives to introduce new contents in games is discussed in [15], but again the objective-based approach (similar to the Goal-Based proposed in this work) was able to generate only level contents.…”

Section: Related Workmentioning

confidence: 91%

Generation of game contents by social media analysis and MAS planning

Amato

Moscato

Xhafa

2019

Computers in Human Behavior

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In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multiplayer, open world gaming, in fact even in "old-school" single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents models.

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Section: Related Workmentioning

confidence: 91%

Generation of game contents by social media analysis and MAS planning

Amato

Moscato

Xhafa

2019

Computers in Human Behavior

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“…For this purpose, we use a representation of the SMB levels we call "micro-patterns", which are thin vertical slices of a level. In previous work, these slices or micro-patterns were used to create levels where "meso-patterns" and "macro-patterns" decided the order of the slices and therefore gave the levels a structure, style and meaning [3,4,5]. Figure 1 shows examples of such slices.…”

mentioning

confidence: 99%

Linear levels through n-grams

Dahlskog

Togelius

Nelson

2014

Proceedings of the 18th International Academic MindTrek Conference: Media Business, Management, Content &Amp; Services

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We show that novel, linear game levels can be created using ngrams that have been trained on a corpus of existing levels. The method is fast and simple, and produces levels that are recognisably in the same style as those in the corpus that it has been trained on. We use Super Mario Bros. as an example domain, and use a selection of the levels from the original game as a training corpus. We treat Mario levels as a left-to-right sequence of vertical level slices, allowing us to perform level generation in a setting with some formal similarities to n-gram-based text generation and music generation. In empirical results, we investigate the effects of corpus size and n (sequence length). While the applicability of the method might seem limited to the relatively narrow domain of 2D games, we argue that many games in effect have linear levels and n-grams could be used to good effect, given that a suitable alphabet can be found.

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“…However, evaluation functions come in all shapes and sizes, and they are all valid with their own set of ups and dows. For instance, they might come from game design concepts such as design patterns [86] or game level metrics [45], or from aesthetic indicators such as symmetry [44], or subjective evaluation from users [87], or even continuously adapting the One of the challenges of generating games [and game content], is that it requires them to be enjoyable and interacted as discussed in the previous section. evaluation based on gameplay [43] or to the designer's preferences [47].…”

Section: Search-based Approachmentioning

confidence: 99%

“…In the former, the focus is on leveraging each agent strengths and where one's weakness, such as lack of knowledge in game design, can be supplied by the other agent. For instance, using game design patterns to help designers build levels [86,134]; thus making these tools more accessible. In the latter, the focus is on providing a plethora of interconnected tools and systems unified in a system that relies on the designer having the complete initiative and expert knowledge to connect the bits that form the design of the game.…”

Section: Mixed-initiative Co-creativitymentioning

confidence: 99%

“…An essential part of EDD is its use of design patterns to hierarchically divide a room into micro-patterns (inventorial and spatial patterns) and their combination into meso-patterns. The design patterns in EDD are based on the work by Bjork and Holopainen [180], Dahlskog et al [181], and Dahlskog and Togelius [86]. While EDD is MI-CC tool to create dungeons for adventure games, we can leverage design patterns to create a generic and domain-independent tool.…”

Section: Design Patternsmentioning

confidence: 99%

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Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

Alvarez¹

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As AI develops, grows, and expands, the more benefits we can have from it. AI is used in multiple fields to assist humans, such as object recognition, self-driving cars, or design tools. However, AI could be used for more than assisting humans in their tasks. It could be employed to collaborate with humans as colleagues in shared tasks, which is usually described as Mixed-Initiative (MI) paradigm. This paradigm creates an interactive scenario that leverage on AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges. For instance, there must be an understanding between humans and AI, where autonomy and initiative become negotiation tokens. In addition, control and expressiveness need to be taken into account to reach some goals. Moreover, although this paradigm has a broader application, it is especially interesting for creative tasks such as games, which are mainly created in collaboration. Creating games and their content is a hard and complex task, since games are content-intensive, multi-faceted, and interacted by external users. Therefore, this thesis explores MI collaboration between human game designers and AI for the co-creation of games, where the AI's role is that of a colleague with the designer. The main hypothesis is that AI can be incorporated in systems as a collaborator, enhancing design tools, fostering human creativity, reducing their workload, and creating adaptive experiences. Furthermore, This collaboration arises several dynamic properties such as control, expressiveness, and initiative, which are all central to this thesis. Quality-Diversity algorithms combined with control mechanisms and interactions for the designer are proposed to investigate this collaboration and properties. Designer and Player modeling is also explored, and several approaches are proposed to create a better workflow, establish adaptive experiences, and enhance the interaction. Through this, it is demonstrated the potential and benefits of these algorithms and models in the MI paradigm.

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Procedural Content Generation Using Patterns as Objectives

Cited by 17 publications

References 9 publications

Generation of game contents by social media analysis and MAS planning

Generation of game contents by social media analysis and MAS planning

Linear levels through n-grams

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

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