This paper attempts to provide a detailed overview of the latest advancements in the field of game difficulty adaptation and experience personalization within the years 2005–2021 from the perspective of dynamic difficulty adjustment and procedural content generation. To this end, a systematic review process was followed in order to search in the database Scopus for relevant work within the addressed research scope. This process resulted in 109 relevant papers, which in turn were taxonomized according to the type of adaptation/personalization approach they employ in order to tailor games to each individual player. The aim of this survey is to address research concepts and problems that are related to the dynamic modeling of the players’ characteristics, the impact of adaptive games on their experience and the dissemination of the relevant research. Findings of this review revealed that the adaptive and personalized games can improve and enhance the experience of the players/students and the rehabilitation of patients through the dynamic modeling of distinct personality characteristics, e.g., personal interests and exhibited emotions.

The manufacturing industry often faces challenges related to customer satisfaction, system degradation, product sustainability, inventory, and operation management. If not addressed, these challenges can be substantially harmful and costly for the sustainability of manufacturing plants. Paradigms, e.g., Industry 4.0 and smart manufacturing, provide effective and innovative solutions, aiming at managing manufacturing operations, and controlling the quality of completed goods offered to the customers. Aiming at that end, this paper endeavors to mitigate the described challenges in a multi-stage degrading manufacturing/remanufacturing system through the implementation of an intelligent machine learning-based decision-making mechanism. To carry out decision-making, reinforcement learning is coupled with lean green manufacturing. The scope of this implementation is the creation of a smart lean and sustainable production environment that has a minimal environmental impact. Considering the latter, this effort is made to reduce material consumption and extend the lifecycle of manufactured products using pull production, predictive maintenance, and circular economy strategies. To validate this, a well-defined experimental analysis meticulously investigates the behavior and performance of the proposed mechanism. Results obtained by this analysis support the presented reinforcement learning/ad hoc control mechanism’s capability and competence achieving both high system sustainability and enhanced material reuse.

The process scheduling is still considered a crucial subject for manufacturing industry, due to the ever-changing circumstances dictated by the nowadays product demand and customer trends. These conditions are often associated with increasing costs and energy consumption, considerably affecting the long-term sustainability of manufacturing plants. To mitigate that effect, one should create an effective strategy tailoring integrated operations and processes to the customer demand and trends faced by the nowadays industry. A well-known approach to this matter is the technologies introduced by manufacturing paradigms, e.g., Industry 4.0 and smart manufacturing. As suggested in literature, these technologies are capable of helping decision-makers by continuously gathering significant information about the state of machinery and manufactured goods. This information is thereafter utilized to identify weaknesses and strengths demonstrated within manufacturing plants. To this end, the present paper presents a process optimization framework implemented in a three-stage production line prone to systematic degradation faults. Aiming at strengthening profitability, the framework engages reinforcement learning with ad-hoc manufacturing/maintenance control in decision-making carried out in implemented machines. Simulation experiments showed improved process planning and inventory management enabling cost-effective green and sustainable manufacturing in manufacturing plants.