PCG/PCGML evaluations: Introducing panda evaluation using the soft launch

Case-based learning (CBL) is an educational method widely used in health professional training, emphasizing theoretical knowledge’s practical application. However, traditional CBL has many challenges related to the complexity and accessibility of case scenarios and its demands on educators to effectively facilitate and evaluate student discussions. Despite its popularity and proven benefits, the comparative effectiveness and efficiency of CBL against newer educational technologies still need to be explored. In this quasi-experimental study, third-year physiotherapy students at the University of Lleida used a computer program for procedural content generation (PCG) and interactive clinical cases (ICs), and assessed them using the Spanish physiotherapy self-efficacy scale and the System Usability Scale, and a satisfaction scale. The study showed a significant improvement in self-efficacy among participants after using PCG and ICs. The usability of the PCG was moderate, while the ICs scored higher and had greater user satisfaction. Satisfaction metrics revealed a strong preference among students for incorporating clinical cases into other subjects, with higher satisfaction levels observed in the IC group compared to the PCG group. The study demonstrated that PCG and ICs significantly enhance clinical skills acquisition in physiotherapy education by improving student performance and engagement.

Procedural Content Generation (PCG) has emerged as a powerful approach for automating game content creation, offering significant benefits in terms of cost reduction and time efficiency compared to traditional game design and development processes (Zhang, Zhang, & Huang, 2022). While Genetic Algorithms (GAs) have been widely used in PCG, alternative metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have demonstrated their effectiveness in delivering high-quality solutions and efficient optimization capabilities across different problem domains (Amato, 2017). However, their application in PCG remains limited. I aim to evaluate the performance of PSO and ABC in map layout generation, challenging the conventional use of GAs. By comparing three metaheuristic algorithms (GA, ABC, and PSO) I seek to assess the effectiveness of these approaches in generating game levels and identify any obvious differences in their performance characteristics. Comprehensive experiments are conducted, applying GA, ABC, and PSO to a map layout generation. Metrics like convergence speed and content quality are used to evaluate the generated game content. My findings reveal that both ABC and PSO demonstrate advantages over traditional GA implementations when generating game levels, indicating their potential for enhancing PCG. In this presentation, I will share the results of comparing three metaheuristic algorithms (GA, PSO, and ABC) in map layout generation for game levels, emphasizing the potential benefits of leveraging diverse algorithmic approaches to create more captivating and immersive game worlds. Also, I will conclude with a call for further research in this area to expose new possibilities in content generation. By considering varied metaheuristic approaches, game developers can improve content generation techniques and create more captivating and interactive player experiences.

Procedural content generation (PCG) systems are designed to automatically generate content for video games. PCG for physics-based puzzles requires one to simulate the game to ensure feasibility and stability of the objects composing the puzzle. The major drawback of this simulation-based approach is the overall running time of the PCG process, as the simulations can be computationally expensive. This paper introduces a method that uses machine learning to reduce the number of simulations performed by an evolutionary approach while generating levels of Angry Birds, a physics-based puzzle game. Our method uses classifiers to verify the stability and feasibility of the levels considered during search. The fitness function is computed only for levels that are classified as stable and feasible. An approximation of the fitness that does not require simulations is used for levels that are deemed as unstable or unfeasible by the classifiers. Our experiments show that naively approximating the fitness values can lead to poor solutions. We then introduce an approach in which the fitness values are approximated with the average fitness value of the levels' parents added to a penalty value. This approximation scheme allows the search procedure to find good-quality solutions much more quickly than a competing approach—we reduce from 43 to 25 minutes the running time required to generate one level of Angry Birds.

Procedural content generation (PCG) is a method of content creation fully or semi-performed by computers. PCG is widely used in game development to generate game content, from Rogue (1998) to No Man's Sky (2016). PCG generates final contents, which are ready to be added to a game, or intermediate contents, which are might work as content sketch to be polished by human designers. In this paper we survey the current state of procedural dungeon generation (PDG) research, a subarea of PCG. We analyzed the works according to the game features they generate, the solution strategy employed and the taxonomy of procedural content generation. Some of the relevant findings of the survey are: (1) PDG for 3D levels has been little explored; (2) few works supported levels with barriers, a game mechanic which blocks, temporarily, the player progression, and; (3) and just a few solutions relied on mixed-initiative approach, where a human design content is combined with a computer generated level.

Procedural Content Generation (PCG) techniques enable automatic creation of diverse and complex environments. While PCG facilitates more efficient content creation, ensuring consistently high-quality, industry-standard content remains a significant challenge. In this research, we propose a method to identify and repair unstable levels generated by existing PCG models. We use Angry Birds as a case study, demonstrating our method on game levels produced by established PCG approaches. Our method leverages object segmentation and visual analysis of level images to detect structural gaps and perform targeted repairs. We evaluate multiple object segmentation models and select the most effective one as the basis for our repair pipeline. Experimental results show that our method improves the stability and playability of AI-generated levels. Although our evaluation is specific to Angry Birds, our image-based approach is designed to be applicable to a wide range of 2D games with similar level structures.

Procedural content generation can be used to create arbitrarily large amounts of game levels automatically, but traditionally the PCG algorithms needed to be developed or adapted for each game manually. Procedural Content Generation via Machine Learning (PCGML) harnesses the power of machine learning to semi-automate the development of PCG solutions, training on existing game content so as to create new content from the trained models. One of the machine learning techniques that have been suggested for this purpose is the autoencoder. However, very limited work has been done to explore the potential of autoencoders for PCGML. In this paper, we train autoencoders on levels for the platform game Lode Runner, and use them to generate levels. Compared to previous work, we use a multi-channel approach to represent content in full fidelity, and we compare standard and variational autoencoders. We also evolve the values of the hidden layer of trained autoencoders in order to find levels with desired properties.

Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games

Procedural content generation (PCG)-based game design aims to reach a new way of playing games by focusing gameplay around algorithmic game content generation. However, positioning interaction with PCG systems and generated content to the center of player experience poses design challenges for both game design and AI design. In order to create the wanted affordances, rich contextual information is required to make informed decisions on the generated content. While previous research has presented excellent developments on PCG’s possibilities, further considering context and affordances in the early stages of prototyping may aid designers reach these possibilities in a more consistent manner. This study is set to discuss how context, affordances and the game’s overall design can be considered during the prototyping process of PCG-based games. Misaligned game context and affordances can result in deeply rooted design issues that may later manifest as subpar gameplay experiences and increased development effort. These emergent issues are examined through a post-mortem case study to produce an extended PCG-based design process, featuring actionable steps, that takes context, affordances, and the game’s overall design into account through meaningful play.

Procedural content generation is helping game developers to create significant quantity of high quality dynamic content for video games at a fraction of cost of the traditional methods. Procedural texture synthesis is a sub category of procedural content generation which helps video games to have significant variations in textures of the environments and the objects across the progress of the game and to avoid repetition. Generative Adversarial Networks are a class of deep learning algorithms which are capable of learning the patterns and creating new patterns. In this paper, Generative Adversarial Networks is used for procedural content generation for original textures synthesis for video game development. This method is used by video game designers for autonomous redesigning of objects and environment textures. This process saves significant time and cost in video game development. The particular attention in this paper is on procedural synthesis of ground surface textures. The generated texture samples are visually acceptable and have mean score of 2.45 with 0.1 standard deviation after 2K iteration. Also the discriminator loss of generated samples reached 0.74 at the final stage of training. The proposed framework can be used as an effective procedural texture synthesis framework for video game design and development.

For high skilled players, an easy game might become boring and for low skilled players, a difficult game might become frustrating. This research's goal is to offer players a personalized experience adapted according to their performance and levels of attention. We created a simple side-scrolling 2D platform game using Procedural Content Generation, Dynamic Difficulty Adjustment techniques and brain computer data obtained from players in real time using an Electroencephalography device. We conducted a series of experiments with different players and got results that confirm that our method is adjusting each level according to performance and attention.

Within the area of procedural content generation (PCG) there are a wide range of techniques that have been used to generate content. Many of these techniques use traditional artificial intelligence approaches, such as genetic algorithms, planning, and answer-set programming. One area that has not been widely explored is straightforward combinatorial search -- exhaustive enumeration of the entire design space or a significant subset thereof. This paper synthesizes literature from mathematics and other subfields of Artificial Intelligence to provide reference for the algorithms needed when approaching exhaustive procedural content generation. It builds on this with algorithms for exhaustive search and complete examples how they can be applied in practice.

The article proposes a method of combining multiple deep forward neural networks to generate a distribution of objects in a 2D scene. The main concepts of machine learning, neural networks and procedural content generation concerning this intention are presented here. Additionally, these concepts are put into the context of computer graphics and used in a practical example of generating an indoor 2D scene. A method of vectorization of input datasets for training forward neural networks is proposed. Scene generation is based on the consequent placement of objects of different classes into the free space defining a room of a certain shape. Several evaluate methods have been proposed for testing the correctness of generation.

We introduce a domain specific language for procedural content generation (PCG) called Grammatical Item Generation Language (GIGL). GIGL supports a compact representation of PCG with stochastic grammars where generated objects maintain grammatical structures. Advanced features in GIGL allow flexible customizations of the stochastic generation process. GIGL is designed and implemented to have direct interface with C++, in order to be capable of integration into production games. We showcase the expressiveness and flexibility of GIGL on several representative problem domains in grammatical PCG, and show that the GIGL-based implementations run as fast as comparable C++ implementation and with less code.

Design and implementation of a survival game towards achieving user’s satisfaction by providing balanced difficulty

Understanding which and how attributes impact player experience can contribute to designing more tailored tools, providing concerns on how to improve these. However, there is a gap in the understanding of what impacts learners’ experience when interacting with Educational Games (EG) featuring Procedural Content generation (PCG) as these have been scantly used together. This article presents an empirical study on which attributes impact both math’s and game’s curiosity of players when interacting with an EG that uses PCG. The results show the attributes that led to higher or lower curiosity, as well aswhich of them are associated with it. Hence, advancing the understanding of what drives players’ curiosity, contributing to the design of EG that feature PCG.