Entertainment Computing最新文献

The fundamental component of language learning is the lexicon. Among various ways employed to facilitate vocabulary development, playing video games can highly contribute to developing vocabulary incidentally. This study examines how playing digital games impacts EFL learners’ incidental vocabulary acquisition. It also investigates gender in digital equity using a digital game, i.e., Minecraft. A total of 73 male and female Iranian pupils between the ages of 9 and 14 were recruited to examine whether incidental vocabulary acquisition through gameplay was effective and to determine whether gender could affect the results. As such, the two-way ANCOVA was run through SPSS to analyze the data. The results indicated that the gamers performed far better than the memorization group. However, no interaction effect between gender and the dependent variable was observed. The implications of the findings can benefit educators and young learners to choose appropriate digital games as supplementary tools for L2 lexical acquisition. The findings contribute to the increasing research on digital games’ potential as an effective tool for promoting vocabulary development.

With the development of intelligent voice and interactive robot technology, new technologies have built a virtual E-learning learning environment that can provide students with an immersive learning experience, making this new learning mode more entertaining. This article investigates the application of entertainment interactive robots based on speech recognition in English artificial intelligence teaching evaluation and automatic feedback. The system has constructed an oral evaluation model based on deep reinforcement learning, which learns the optimal behavioral strategies through interaction with the environment. The model will train through oral conversations with learners to learn how to accurately evaluate oral proficiency and provide relevant feedback. After the construction of the system is completed, the accuracy and efficiency of the system are improved by adjusting the parameters of the model, increasing the diversity of training data, and improving the user interface and interaction mode based on user feedback, making it more friendly and easy to use. The experimental results show that the English oral evaluation and automatic feedback system designed in this paper based on deep reinforcement learning and speech recognition algorithms has high accuracy and efficiency. The system can accurately evaluate learners’ oral proficiency and provide personalized learning suggestions based on individual differences.

Increasing technology advancements have led to a number of problems with modern corporate decision-making, which is a challenging occurrence in the absence of business intelligence and machine learning (ML). Because effective decision-making is impossible without ML, integration of ML with business intelligence (BI) is essential to both corporate decision-making and business intelligence as a whole. Only once they have learned anything again may machines assist in your educational process. This study suggests a fresh approach to knowledge building in company management decision-making through the use of gaming and machine learning models. Using a game model that involves decision-making, knowledge analysis based on business management is conducted. Subsequently, quantum reinforcement reward neural networks build knowledge. The accuracy, precision, recall, F_1 score, MSE, NSE of business management modelling with knowledge growth are all assessed by simulation. The student’s gender had no bearing on the income they were offered throughout the job placement process or their MBA specialisations in Marketing and Finance (Mkt & Fin) or Marketing and Human Resource (Mkt & HR), according to a statistical t-test with a significance threshold of 0.05 (p > 0.05).

Currently, the design of virtual ecological landscapes in theme parks has become an innovative form of entertainment. This study aims to explore how to construct virtual ecological landscapes in theme parks through entertainment robots and VR devices, in order to provide an interactive entertainment experience between tourists and nature. We have built an outdoor sensing system and collected environmental data through sensors. Implement motion control of entertainment robots through programming, enabling them to interact according to environmental changes. Then, through information control and feedback technology, interaction with tourists is achieved. Analyze the motion trajectory of robots to optimize their performance, use virtual reality technology for design and rendering, achieve interactive effects through VR development platforms, and optimize VR devices through controlled simulation technology to enhance the virtual experience of tourists. Finally, combining the principles of ecological landscape design with landscape design techniques, applying VR design technology to achieve the design of virtual ecological landscapes in theme parks, considering the layout and combination of natural elements, in order to create virtual landscapes that are similar to real ecology. Through the application of VR technology, tourists can experience the landscape changes under different seasons and weather conditions, increasing the fun and realism of interaction. The results indicate that the virtual ecological landscape design of the theme park has been achieved through the combination of entertainment robots and VR devices. Tourists can obtain an immersive entertainment experience through interaction with robots and the application of virtual reality technology. Through robot trajectory analysis and ecological landscape recognition technology, the landscape design effect is optimized.

This study showed that using collaborative entertainment robots for human-robot interaction can be a promising way to help manage the health of ageing populations by automatically detecting and mitigating cognitive impairment. The system enhanced spoken interaction with users by using cutting-edge technologies such as state-of-the-art speech recognition, natural language processing, and machine learning. The system was tested on senior participants and gathered, analyzed, and displayed individual interaction models to provide automated user engagement, daily interaction monitoring, and automatic early detection of deteriorating mental health. The findings were presented using bar charts and confusion matrices, incorporating important metrics such as mental workload and speech/non-speech interaction graphic. These visualizations aided individuals in managing their behavior to achieve an optimal cognitive workload, a challenging measure to determine due to cognitive decline. In order to make significant progress in the subject, future advancements need to focus on addressing the unpredictability in human speech sequences, using non-speech modalities such as gestures or facial expressions as supplementary inputs to complement speech and behavior, and effectively managing concerns related to human rights and data protection. In addition to technological constraints, future research should prioritize the examination of the enduring impacts of cognitive therapies facilitated by entertainment robots.

This article presents a novel and highly interactive process to generate natural language narratives based on our ongoing work on semiotic relations, providing four criteria for composing new narratives from existing stories. The wide applicability of this semiotic reconstruction process is suggested by a reputed literary scholar’s deconstructive claim that new narratives can often be shown to be a tissue of previous narratives. Along, respectively, three semiotic axes – syntagmatic, paradigmatic, and meronymic – existing stories can yield new stories by the combination, imitation, or expansion of an iconic scene; lastly, a new story may emerge through reversal via an antithetic consideration, i.e., through the adoption of opposite values. Targeting casual users, we present a fully operational prototype with a simple and user-friendly interface that incorporates an AI agent, namely ChatGPT. The prototype, in a coauthor capacity, generates context-compatible sequences of events in storyboard format using backward-chaining abductive reasoning (employing Stable Diffusion to draw scene illustrations), conforming as much as possible to the user’s authorial instructions. The extensive repertoire of book and movie summaries available to the AI agent obviates the need to manually supply laborious and error-prone context specifications. A user study was conducted to evaluate user experience and satisfaction with the generated narratives. The preliminary findings suggest that our approach has the potential to enhance story quality while offering a positive user experience.

Educational games rapidly integrate entertainment technology and learning, engaging individuals in dynamic educational experiences. These games incorporate multimedia content to encourage critical thinking, problem-solving and information retention. Educational games employ immersive technology such as virtual and augmented reality to transfer individuals to simulated worlds, hence improving learning. Furthermore, artificial intelligence (AI) technologies optimize educational experiences by adjusting information to individual learning styles, providing focused feedback as well as encouraging a more effective and entertaining learning technology. The integration of educational games with immersive and AI technology provides great potential for transforming how individuals acquire and apply information sharing. This research determined the creation of significant educational applications that are personalized and adaptive through the use of image, emotional recognition and speech, intelligent agents that replicate the effects of an individual opponent and control over the complexities of game levels along with information. The study evaluated the different tools that educators and learners could utilize to develop immersive and artificial intelligence-based instructional games without a requirement for programming knowledge. The study demonstrates that immersive technology and AI technology could represent beneficial resources for creating educational video games and entertainment technology. The research highlights the novel possibilities of stochastic swing golf optimization (SSGOA) immersive and AI technologies providing an innovative approach to developing effective as well as attractive learning environments.

Currently, there are a significant and growing number of games and players. Creating digital games becomes a challenging task, as manual game development is costly and time-consuming. A technique known as procedural content generation (PCG) can potentially reduce both the time and production costs of games. It is feasible to automate the creation process by utilizing artificial intelligence techniques and PCG, assisting game designers in their tasks. PCG is not a novel concept, and there is a diverse range of algorithms aimed at automatically generating content in games. However, a significant number of these techniques do not incorporate artificial intelligence. This paper introduces the PCGRLPuzzle framework used to generate procedural scenarios through reinforcement learning agents trained with the policy proximal optimization algorithm. The process of building scenarios poses a challenging problem due to the existence of an exponential number of possibilities. The framework employs a mixed-initiative design, where humans and computers collaborate to create levels for 2D dungeon crawler games. We apply this framework to generate levels for three different games and analyze the results based on their expressive range, evaluating linearity and lenience. The conducted experiments demonstrate that utilizing reinforcement learning in conjunction with procedural content generation and mixed-initiative enables the generation of highly diverse levels.