The rapid expansion of the Internet of Things (IoT) has necessitated the development of efficient and reliable routing protocols to facilitate communication between diverse devices and platforms. Swarm intelligence, derived from the collective behavior of social insects, offers a promising approach to inspire novel routing strategies. This paper explores the application of swarm intelligence principles in the design of routing protocols for IoT networks. We discuss the fundamental concepts of swarm intelligence, emphasizing the key mechanisms such as foraging, self-organization, and collective decision-making. The paper evaluates several swarm intelligence-based routing protocols, highlighting their performance in terms of energy efficiency, reliability, and adaptability. We analyze the pros and cons of these protocols and provide insights into their potential challenges. Furthermore, we discuss the integration of swarm intelligence with existing IoT network architectures, highlighting the benefits and future research directions. The findings reveal that swarm intelligence-based routing protocols have the potential to significantly enhance the performance of IoT networks by optimizing resource allocation, reducing communication overhead, and improving fault tolerance.

This paper explores the application of deep reinforcement learning (DRL) techniques in the field of real-time strategy (RTS) games. Real-time strategy games are complex, dynamic environments that require players to make rapid decisions under uncertainty. DRL has emerged as a powerful tool for training intelligent agents capable of learning optimal strategies through self-play. The focus of this study is to investigate the effectiveness of DRL algorithms in simulating human-like decision-making and developing competitive RTS agents. We detail the design and implementation of a novel DRL framework tailored for RTS games, which incorporates a combination of reinforcement learning and neural network architectures. The proposed framework is evaluated against a range of well-known RTS games, demonstrating significant improvements in the agents' performance and adaptability. Furthermore, we analyze the computational efficiency and stability of the DRL algorithms, illustrating their potential for real-time deployment in competitive gaming scenarios. This research contributes to the advancement of AI in the domain of RTS games, showcasing the potential of DRL to enhance the gaming experience and inspire further research in the intersection of AI and interactive media.

This paper investigates the potential of Intelligent Tutoring Systems (ITS) in fostering adaptive learning environments. ITS are computer-based applications designed to assist learners in acquiring knowledge and skills by adapting to their individual needs and learning styles. The increasing use of technology in education has opened up new avenues for personalized learning experiences, and ITS play a crucial role in this transformation. The aim of this study is to explore the various features and functionalities of ITS, such as intelligent feedback, personalized learning paths, and self-regulation support, and to analyze their effectiveness in enhancing learning outcomes. By integrating artificial intelligence and machine learning techniques, ITS can dynamically adjust the learning content and pace according to the learners' progress and performance. The paper also discusses the challenges in implementing ITS and the potential benefits for both students and educators. The findings suggest that ITS can significantly improve the quality of education, making it more inclusive, efficient, and tailored to individual learners' needs.