Exploring Level Blending across Platformers via Paths and Affordances

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.Comment: 6 pages, 5 figures, 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020

Can people guess what happened to others from their reactions?

Are we able to infer what happened to a person from a brief sample of his/her behaviour? It has been proposed that mentalising skills can be used to retrodict as well as predict behaviour, that is, to determine what mental states of a target have already occurred. The current study aimed to develop a paradigm to explore these processes, which takes into account the intricacies of real-life situations in which reasoning about mental states, as embodied in behaviour, may be utilised. A novel task was devised which involved observing subtle and naturalistic reactions of others in order to determine the event that had previously taken place. Thirty-five participants viewed videos of real individuals reacting to the researcher behaving in one of four possible ways, and were asked to judge which of the four ‘scenarios’ they thought the individual was responding to. Their eye movements were recorded to establish the visual strategies used. Participants were able to deduce successfully from a small sample of behaviour which scenario had previously occurred. Surprisingly, looking at the eye region was associated with poorer identification of the scenarios, and eye movement strategy varied depending on the event experienced by the person in the video. This suggests people flexibly deploy their attention using a retrodictive mindreading process to infer events

Force Characterization and Manufacturing of a Dynamic Unilateral Clubfoot Brace

Clubfoot is a musculoskeletal birth defect characterized by an inward twisting of an infant’s feet. Currently, a series of casts are used to correct clubfoot and the Steenbeek brace is used to maintain the correction. However, this method has issues with compliance, comfort, and social stigma. Mr. Jerald Cunningham, CPO, designed and is utilizing a unilateral clubfoot maintenance brace called the Cunningham Clubfoot Brace. He expects his brace to reduce treatment time, lessen social stigma, and increase child mobility. Hope Walks, in Kijabe, Kenya, is interested in implementing this new maintenance brace at their clinics. However, there is not enough published research on its biomechanics and patient success rates to confirm Mr. Cunningham’s findings. The Cunningham Clubfoot Brace Collaboratory project seeks to validate the effectiveness of this design through biomedical testing and increase brace accessibility through sustainable manufacturing. The team is measuring the biomechanical forces applied by the brace with multiple force sensors on the Cunningham and Steenbeek braces. Mr. Cunningham plans to use injection molding to increase brace production. The team is completing Finite Element Analysis to determine how the brace’s properties change with injection molding. The team is also completing fatigue analysis with the Cunningham Brace to quantify its reusability. Furthermore, the clinical study in Kenya and Dr. Emily Farrar’s retrospective research paper will contribute to the published research on the Cunningham Brace. The collaborative efforts of the team will increase further understanding of the Cunningham Brace and its acceptance as an alternative clubfoot maintenance brace.https://mosaic.messiah.edu/engr2022/1002/thumbnail.jp

Deep learning for procedural content generation

Summarization: Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.Presented on: Neural Computing and Application

The Validity of d′ Measures

Subliminal perception occurs when prime stimuli that participants claim not to be aware of nevertheless influence subsequent processing of a target. This claim, however, critically depends on correct methods to assess prime awareness. Typically, d′ (“d prime”) tasks administered after a priming task are used to establish that people are unable to discriminate between different primes. Here, we show that such d′ tasks are influenced by the nature of the target, by attentional factors, and by the delay between stimulus presentation and response. Our results suggest that the standard d′ task is not a straightforward measure of prime visibility. We discuss the implications of our findings for subliminal perception research

Effects of episodic future thinking and self-projection on children’s prospective memory performance

The present study is the first to investigate the benefits of episodic future thinking (EFT) at encoding on prospective memory (PM) in preschool (age: M = 66.34 months, SD = 3.28)and primary school children (age: M = 88.36 months, SD = 3.12). A second aim was to examine if self-projection influences the possible effects of EFT instructions. PM was assessed using a standard PM paradigm in children with a picture-naming task as the ongoing activity in which the PM task was embedded. Further, two first- and two second-order ToM tasks were administered as indicator of children’s self-projection abilities. Forty-one preschoolers and 39 school-aged children were recruited. Half of the participants in each age group were instructed to use EFT as a strategy to encode the PM task, while the others received standard PM instructions. Results revealed a significant age effect, with school-aged children significantly outperforming preschoolers and a significant effect of encoding condition with overall better performance when receiving EFT instructions compared to the standard encoding condition. Even though the interaction between age group and encoding condition was not significant, planned comparisons revealed first evidence that compared to the younger age group, older children’s PM benefited more from EFT instructions during intention encoding. Moreover, results showed that although self-projection had a significant impact on PM performance, it did not influence the effects of EFT instructions. Overall, results indicate that children can use EFT encoding strategies to improve their PM performance once EFT abilities are sufficiently developed. Further, they provide first evidence that in addition to executive functions, which have already been shown to influence the development of PM across childhood, self-projection seems to be another key mechanism underlying this development

Levels from Sketches with Example-Driven Binary Space Partition

Procedural content generation via machine learning (PCGML) has been demonstrating its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. In this paper we present an example-driven adaptation of a classic PCG approach, binary space partition (BSP), that takes a structural template or sketch of a level and fills in the details from examples. We show that this example-driven adaptation can generate a diverse set of levels from a single structural template. We evaluate the levels generated in terms of difference between paths through the levels, amount of the level copied from the examples, and other common PCG level evaluation metrics. Furthermore, we compare this method to a Markov chain approach and show that our BSP approach matches the training level distribution better while generating a greater range of interesting features