This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar ...
ISBN:
(纸本)9798400709449
This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar network model architectures were evaluated and compared to maximize the learning efficiency of our AI agent. The trained AI demonstrates commendable performance and the ability to learn and adapt strategies into gameplay. However, while the AI agent displayed potential in mastering gameplay dynamics, there remain several challenges to integrating the agent to complete commercial bullet hell games. These challenges may provide directions for future research.
Phishing is a well-known cybersecurity attack that has rapidly increased in recent years. It poses legitimate risks to businesses, government agencies, and all users due to sensitive data breaches, subsequent financia...
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Enabling preserving bisimilarity is a refinement of strong bisimilarity that preserves safety as well as liveness properties. To define it properly, labelled transition systems needed to be upgraded with a successor r...
As e-business is invading the service industry and the public sector, there is a need for a service-flow management of those processes, which consist of a sequence of interrelated sub-services. Making use of the poten...
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Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to the...
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This paper presents a method for representing trees using constraint logic programming over finite domains. We describe a class of trees that is of particular interest to us and how we can represent the set of trees b...
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In this paper, we propose an integrated framework to formally specify the syntax and the semantics of domain-specific languages. We build this framework by integrating the Microsoft DSL Tools, a framework to develop g...
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Video streaming is growing in popularity and has become the most bandwidth-consuming Internet service. As such, robust streaming in terms of low latency and uninterrupted streaming experience, particularly for viewers...
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In recent years, the exponential growth of e-commerce has transformed consumer purchasing behavior, with online reviews playing a crucial role in shaping buying decisions. Unfortunately, the prevalence of manipulated ...
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ISBN:
(纸本)9798350341737
In recent years, the exponential growth of e-commerce has transformed consumer purchasing behavior, with online reviews playing a crucial role in shaping buying decisions. Unfortunately, the prevalence of manipulated reviews has become a significant challenge, undermining the integrity of consumer feedback and eroding trust in e-commerce platforms. This research paper focuses on developing an advanced solution to address the issue of manipulated reviews using the innovative language model, GPT-4. The primary objective of this study is to investigate the effectiveness of GPT-4 in identifying and flagging manipulated reviews within the context of e-commerce platforms. GPT-4 is a state-of-the-art language model with superior natural language processing capabilities, making it an ideal candidate for automated review analysis. The research methodology encompasses a large-scale data collection process, where diverse e-commerce reviews are gathered from various platforms. To simulate real-world scenarios, manipulated reviews are artificially injected into the dataset, representing different degrees of sophistication. The dataset is then annotated by expert reviewers to establish ground truth labels for comparison. Next, GPT-4 is fine-tuned using transfer learning to specialize in detecting manipulated reviews. The fine-tuning process involves exposing the model to both genuine and manipulated review samples, allowing it to learn patterns and features indicative of manipulation. The fine-tuned model's performance is then evaluated using various metrics, including precision, recall, F1 score, and accuracy, against the ground truth dataset. The results of the experiment demonstrate the efficacy of GPT-4 in distinguishing between authentic and manipulated reviews. GPT-4 showcases remarkable accuracy and robustness in detecting increasingly sophisticated manipulation techniques, outperforming previous iterations of language models and traditional detection methods. Furthermore,
This paper proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to min...
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This paper proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to minimize the morphology design while the second objective is to maximize the performance of the SAWR in performing the climbing motion. Results show that the proposed MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance. The Pareto set of optimal solutions provide users a choice of solutions for trade-off between the two objectives.
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