Procedural Content Generation via Knowledge Transformation (PCG-KT)

Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future.

Although game‐based learning has been increasingly promoted in education, there is a need to adapt game content to individual needs for personalized learning. Procedural content generation (PCG) offers a solution for difficulty in developing game contents automatically by algorithmic means as it can generate individually customizable game contents applicable to various objectives. In this paper, we advanced a data‐driven PCG approach benefiting from a genetic algorithm and support vector machines to automatically generate educational‐game contents tailored to individuals' abilities. In contrast to other content generation approaches, the proposed method is not dependent on designer's intuition in applying game contents to fit a player's abilities. We assessed this data‐driven PCG approach at length and showed its effectiveness by conducting an empirical study of children who played an educational language‐learning game to cultivate early English‐reading skills. To affirm the efficacy of our proposed method, we evaluated the data‐driven approach against a heuristic‐based approach. Our results clearly demonstrated two things. First, users realized greater performance gains from playing contents tailored to their abilities compared with playing uncustomized game contents. Second, this data‐driven approach was more effective in generating contents closely matching a specific player‐performance target than the heuristic‐based approach.

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.

The generation of desirable video game contents has been a challenge of games level design and production. In this research, we propose a game player flow experience driven interactive latent variable evolution strategy incorporated with a Deep Convolutional Generative Adversarial Network (DCGAN) for undertaking game content generation with respect to a 2D Super Mario video game. Since the Generative Adversarial Network (GAN) models tend to capture the high-level style of the input images by learning the latent vectors, they are used to generate game scenarios and context images in this research. However, as GANs employ arbitrary inputs for game image generation without taking specific features into account, they generate game level images in an incoherent manner without the specific playable game level properties, such as a broken pipe in the Mario game level image. In order to overcome such drawbacks, we propose a game player flow experience driven optimised mechanism with human intervention, to guide the game level content generation process so that only plausible and even enjoyable images will be generated as the candidates for the final game design and production.

Theoretical and methodological principles are substantiated and practical recommendations for managing the transformation of organizational knowledge as a tool for effective use of industrial enterprises intellectual potential in order to increase its competitive advantages are developed in this paaper. Theoretical generalization of industrial enterprises organizational knowledge concept is carried out. It is proved that the hidden staff knowledge acquires special value for the enterprise. The own vision of the term «organizational knowledge» is proposed. The author's classification of organizational knowledge by the degree of their manifestation and formalization is developed. The scientific value of the proposed classification is that it more deeply and accurately considers the consistent transformation of some types of knowledge into others, reflecting the dynamics of knowledge transformation process, determining the need for constant updating, and, consequently – creating new knowledge and its immediate implementation. Knowledge types matrix is constructed. The structure of organizational knowledge reflecting not their plane, but their spatial nature is determiined. According to the types and structure of organizational knowledge, the forms of their manifestation are defined. The transformation of knowledge not only by types, but also by the transition of knowledge from the individual level to the group and organizational levels is substantiated. At the same time the process of knowledge synergy is clearly represented. Recommendations for improving the processes of knowledge transformation in industrial enterprises are developed, as well as mechanisms for their practical implementation are proposed. The main conclusions and recommendations can serve as theoretical, methodological and methodical basis for further research in closely related areas of enterprise personnel management and innovation management. Obtained results form the principles of the organizational and economic mechanism for ensuring the effective use of intellectual potential of industrial enterprises in the implementation of European vector of national economy development.

The best practice of watershed management is through the understanding of the hydrological processes. As a matter of fact, hydrological processes are highly associated with stochastic, non-linear, and non-stationary phenomena. Hydrological processes simulation and modeling are challenging issues in the domains of hydrology, climate and environment. Hence, the development of machine learning (ML) models for solving those complex hydrological problems took essential place over the past couple decades. It can be observed, hydrological data availability has increased remarkably, and thus computational resources has led to a resurgence in ML models’ development. It has been witnessed huge efforts on the hydrological processes modeling using the facility of ML models and several review researches have been conducted. Literature studies approved the capacity of ML models in the field of hydrology over the classical “traditional models” based on their forecastability, flexibility, precision, generalization, and modeling execution convergence speed. However, although several potential merits were observed in ML model’s development, several limitations are allied such as the interpretability of those black-box models, the practicality of the ML models in watershed management, and difficulty to explain the physical hydrological processes. In this survey, an exhibition for all the published review articles on the development of ML models for hydrological processes and recognize all the research gaps and potential research direction. The ultimate aim of the current survey is to establish a new milestone for the interested hydrology, environment and climate researchers on the applications of ML models.

Machine learning algorithms provide the ability to quickly adapt and find patterns in large diverse data sources and therefore are a potential asset to application developers in enterprise systems, networks, and security domains. They make analyzing the security implications of these tools a critical task for machine learning researchers and practitioners alike, spawning a new subfield of research into adversarial learning for security-sensitive domains. The work presented in this book advanced the state of the art in this field of study with five primary contributions: a taxonomy for qualifying the security vulnerabilities of a learner, two novel practical attack/defense scenarios for learning in real-world settings, learning algorithms with theoretical guarantees on training-data privacy preservation, and a generalization of a theoretical paradigm for evading detection of a classifier. However, research in adversarial machine learning has only begun to address the field's complex obstacles—many challenges remain. These challenges suggest several new directions for research within both fields of machine learning and computer security. In this chapter we review our contributions and list a number of open problems in the area. Above all, we investigated both the practical and theoretical aspects of applying machine learning in security domains. To understand potential threats, we analyzed the vulnerability of learning systems to adversarial malfeasance. We studied both attacks designed to optimally affect the learning system and attacks constrained by real-world limitations on the adversary's capabilities and information.We further designed defense strategies, which we showed significantly diminish the effect of these attacks. Our research focused on learning tasks in virus, spam, and network anomaly detection, but also is broadly applicable across many systems and security domains and has farreaching implications to any system that incorporates learning. Here is a summary of the contributions of each component of this book followed by a discussion of open problems and future directions for research. Framework for Secure Learning The first contribution discussed in this book was a framework for assessing risks to a learner within a particular security context (see Table 3.1). The basis for this work is a taxonomy of the characteristics of potential attacks. From this taxonomy (summarized in Table 9.1), we developed security games between an attacker and defender tailored to the particular type of threat posed by the attacker.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

Cross-domain network attack detection enabled by heterogeneous transfer learning

The analysis of the content of posts written on social media has established an important line of research in recent years. The study of these texts, as well as their relationship with each other and their dependence on the platform on which they are written, enables the behavior analysis of users and their opinions with respect to different domains. In this work, a hybrid machine learning-based system has been developed to classify texts using topic modeling techniques and different word-vector representations, as well as traditional text representations. The system has been trained with ride-hailing posts extracted from Reddit, showing promising performance. Then, the generated models have been tested with data extracted from other sources such as Twitter and Google Play, classifying these texts without retraining any models and thus performing Transfer Learning. The obtained results show that our proposed architecture is effective when performing Transfer Learning from data-rich domains and applying them to other sources.

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

This article discusses the concept of homework in cognitive-behavioral therapy (CBT), and reviews the articles in this special issue of Cognitive Therapy and Research. The article underscores the pivotal role of between- session activities and demonstrates that this role has been recognized for many years. This article reviews the articles from this special issue and uses them as a springboard to review other research and then discuss potential directions for future theory and research. The initial research in this area focused on documenting the magnitude of the relationship between homework adherence and outcome in CBT. Research has now advanced to incorporate studies that examine issues such as homework competence, the complicated relationship between homework and other aspects of CBT such as the therapeutic relationship, and the use of technology to try to enhance homework completion and clinical outcomes. The use of homework assignments in CBT remains a critical issue for development. Limitations of the literature are addressed, and directions for future research in the field are provided.

This article analyses the field of research on information and communication technology (ICT) in education. It reviews the results of ICT researches carried out in the world. First, the article presents the retrospective of researches on ICT implementation into education. Summarizing them, it reviews main research findings in five areas of ICT implementation: 1) the impact of ICT use on students achievements and attitudes; 2) the impact of ICT use on teachers; 3) the effect of teachers' factors and instructional methods on ICT efficiency; 4) the influence of infrastructure and organizational factors on ICT efficiency; 5) the effect of specific software design features. Then, the paper reviews the main recent and future directions of researches on ICT in education, in the world. Finally, it shortly reviews the most important future directions of ICT research and development in Lithuania. The article concludes that in spite of numerous ICT studies worldwide the national research and experiments have to be carried out in order to implement ICT in an appropriate for Lithuania and efficient way.

Cancer informatics is a multidisciplinary field of research. It includes oncology, pathology, radiology, computational biology, physical chemistry, computer science, information systems, biostatistics, machine learning, artificial intelligence (AI), data mining and many others. Machine learning (ML) offers potentially powerful tools, intelligent methods, and algorithms that can help in solving many medical and biological problems. The variety of ML algorithms enable the design of a robust techniques and new methodologies for managing, representing, accumulating, changing, discovering, and updating knowledge in cancer-based systems. Moreover it supports learning and understanding the mechanisms that will help oncologists, radiologists and pathologists to induce knowledge from cancer information databases. This paper presents the research results of the author and his colleagues that have been carried out in recent years on using machine learning in cancer informatics. In addition the talk discusses several directions for future research.