Relevant bibliographies by topics / Emotional intelligent agent driving

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Emotional intelligent agent driving'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Emotional intelligent agent driving"

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Ajansondkar, Atharva S., Samarth A. Bachate, Gyanoba S. Bhange, Yash S. Gwalvanshi, and Prof. Amol T. Take. "Developing an Intelligent Conversational Agent for Mental Health Support." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 375–83. https://doi.org/10.5281/zenodo.14026556.

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The use of technology in mental health support has grown essential, providing new avenues to address accessibility and privacy concerns. In this paper, we present an intelligent conversational agent designed to offer mental health support by using AI and reinforcement learning. The agent is equipped with Natural Language Processing (NLP) capabilities, enabling it to interpret user emotions and respond empathetically. Leveraging the RAG (Retrieval-Augmented Generation) model, this system enhances the relevance and quality of interactions, making it a more adaptive tool for those who need immedi
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Demir, Cansu, Alexander Meschtscherjakov, and Magdalena Gärtner. "Unlocking Trust and Acceptance in Tomorrow’s Ride: How In-Vehicle Intelligent Agents Redefine SAE Level 5 Autonomy." Multimodal Technologies and Interaction 8, no. 12 (2024): 111. https://doi.org/10.3390/mti8120111.

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As fully automated vehicles (FAVs) advance towards SAE Level 5 automation, the role of in-vehicle intelligent agents (IVIAs) in shaping passenger experience becomes critical. Even at SAE Level 5 automation, effective communication between the vehicle and the passenger will remain crucial to ensure a sense of safety, trust, and engagement. This study explores how different types and combinations of information provided by IVIAs influence user experience, acceptance, and trust. A sample of 25 participants was recruited for the study, which experienced a fully automated ride in a driving simulato
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Amaresha Prasad Sahoo. "Dynamic routing algorithms in customer support: Revolutionizing contact center operations." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2976–83. https://doi.org/10.30574/wjaets.2025.15.2.0880.

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Dynamic routing algorithms have transformed contact center operations through AI-driven decision-making and real-time data analytics. These systems optimize customer-agent matching while enhancing operational efficiency through intelligent queue management and predictive analytics. The integration of machine learning, natural language processing, and sentiment analysis capabilities has revolutionized how contact centers handle customer interactions, leading to improved resolution rates and customer satisfaction. Cloud-native solutions and emerging technologies continue to advance routing capab
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Sreenivasul Reddy Meegada. "Impact of customer experience from traditional IVR to virtual assistants in contact centers." Global Journal of Engineering and Technology Advances 23, no. 1 (2025): 097–102. https://doi.org/10.30574/gjeta.2025.23.1.0099.

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The evolution of customer experience in contact centers has undergone a remarkable transformation with the transition from traditional Interactive Voice Response (IVR) systems to Natural Language Processing (NLP)-powered Virtual Assistants. This article explores the fundamental limitations of conventional IVR technologies that have led to customer frustration, including navigational complexity, lack of personalization, cognitive burden on users, and emotional disconnection. The integration of advanced NLP capabilities has revolutionized customer interactions by enabling more intuitive engageme
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Khulood Abu Maria. "Bridging the Gap: AI-Driven Agent-Based Systems in Modern Software Engineering." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 839–48. https://doi.org/10.52783/jisem.v10i43s.8486.

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Agent-based systems (ABS) are the newest and most effective approach in software engineering to solve complex, chaotic, and interconnected problems. Since ABS models the systems as the agents that communicate with each other, it is an effective approach to creating flexible software. This paper aims to discuss the possibility of applying agent-based systems to software engineering with a focus on the opportunities and challenges and further research directions. This paper presents a conceptual model that demonstrates the connections between the agents, their surroundings, and the software engi
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Nourkova, Veronika V. "Cultural development of empathy-identification and empathy-modeling." National Psychological Journal 40, no. 4 (2020): 3–17. http://dx.doi.org/10.11621/npj.2020.0401.

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Background. The article develops the concept of the originality of the functions, structure and genesis of empathy-identification (EI) and empathy-modeling (EM). EI is viewed as a psychological tool for organizing joint activities such as a mono-role coalition, which has developed in anthropogenesis, and consists in the ability to appropriate the emotional states of another person. In contrast, EM is a cultural technology for maintaining joint activity such as poly-role coordination, which requires the subject of activity to create and maintain in consciousness a dynamic representation of the
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Giridhar Raj Singh Chowhan. "AI-Powered Virtual Agents in CRM: Revolutionizing Customer Interaction and Service Automation." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 245–52. https://doi.org/10.32996/jcsts.2025.7.3.27.

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This article examines the transformative impact of AI-powered virtual agents on Customer Relationship Management systems. It explores how these sophisticated software entities are fundamentally redefining customer interactions and service automation across industries. It traces the evolution of CRM systems through four distinct generations, culminating in today's AI-driven solutions that leverage natural language processing, machine learning, and sentiment analysis to deliver personalized customer experiences. It details how virtual agents enhance omnichannel engagement, enable intelligent sel
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Harrington, Charles, and Burkhard Jaeger. "Social Innovation, Transformation Management, And Adaptive Leadership." International Journal of Business & Management Studies 06, no. 06 (2025): 1–8. https://doi.org/10.56734/ijbms.v6n6a1.

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This paper explores the dynamic interplay between social innovation, adaptive leadership, and transformation management in addressing complex societal challenges. Social innovation is defined as the development of novel, effective, and sustainable solutions to pressing social issues such as poverty, inequality, and climate change. It emphasizes collaborative approaches like co-creation, design thinking, systems thinking, and social entrepreneurship to foster inclusive and impactful change. The paper traces the historical evolution of social innovation, highlighting its roots in ancient civiliz
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Chen, Wei, Mingyu Yu, and Jia Hou. "Synergistic Relationship, Agent Interaction, and Knowledge Coupling: Driving Innovation in Intelligent Construction Technology." Buildings 14, no. 2 (2024): 542. http://dx.doi.org/10.3390/buildings14020542.

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The core driving force behind innovation in intelligent construction technology is synergistic relationships. It has become common practice to promote synergistic innovation through agent interaction and knowledge coupling in the development of intelligent construction technology. Drawing upon synergetics, social network theory, and the knowledge base view as theoretical frameworks, this research examines the impact of synergistic relationship, agent interaction, and knowledge coupling on innovation in intelligent construction technology. An empirical analysis of 186 questionnaires revealed th
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Pan, Xingye. "A Conceptual Framework on AI-Driven Consumer Behavior in the Age of Digital Branding." Frontiers in Business, Economics and Management 19, no. 3 (2025): 58–64. https://doi.org/10.54097/3aasrp70.

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As artificial intelligence deepens into digital brand interaction, understanding its psychological and relational impact on consumers has become increasingly important. This conceptual paper proposes a comprehensive framework that links artificial intelligence perception, trust formation, and consumer behavior outcomes. This model goes beyond a purely technical perspective and emphasizes how consumers interpret artificial intelligence agents through perceived authenticity, intelligence, and social emotional cues. Trust - including cognitive trust and emotional trust - becomes a key mediating m
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Dissertations / Theses on the topic "Emotional intelligent agent driving"

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Nickolaenko, A. V., and D. O. Marchenko. "Affective intelligent driving agent (AIDA)." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31087.

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It’s impossible to imagine modern world without using motor cars. AIDA aims to change the way we interact with our car. MIT(Massachusetts Institute of Technology) researchers and designers are developing the Affective Intelligent Driving Agent (AIDA) - a new in-car personal robot that brings a revolution to life of all motor-cars’ users. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/31087
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Gustavsson, Linus. "Dynamically Adaptive Intelligent Agents in Driving Simulator Environments." Thesis, Linköping University, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10361.

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<p>In this thesis work I have been working with two traffic simulators called Hank and ST Software. Hank is a research tool at the University of Iowa and ST Software is a commercial product. To evaluate which of these is the most suitable for behavior research I have implemented three types of intelligent agents: Overtaking Agent, Traffic Light Agent and Meeting Agent. The thesis work was extended by adding the possibility for realistic human behavior to the agents.</p><p>The result indicated that Hank allowed for greater control over behavior while ST Software allowed for faster and easier im
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Foen, Nancy (Nancy V. ). "Exploring the human-car bond through an Affective Intelligent Driving Agent (AIDA)." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77002.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 109-110).<br>As the amount of time spent inside vehicles increases, there is an ever more pressing need for safe interfaces that support drivers while they are multi-tasking. We present an Affective Intelligent Driving Agent (AIDA), a sociable robot designed to sit at the dashboard of a vehicle and behave as a friendly assistant. This highly expressive robot was designed to receive signals from t
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Won, Jong-Seob. "Intelligent energy management agent for a parallel hybrid vehicle." Texas A&M University, 2004. http://hdl.handle.net/1969.1/271.

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This dissertation proposes an Intelligent Energy Management Agent (IEMA) for parallel hybrid vehicles. A key concept adopted in the development of an IEMA is based on the premise that driving environment would affect fuel consumption and pollutant emissions, as well as the operating modes of the vehicle and the driver behavior do. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver, and the operating mode (and trend) of the vehicle using long and short term statistical features of the drive cycle. This
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Bresó, Guardado Adrián. "A Computational Framework for Planning Therapeutical Sessions aimed to Support the Prevention and Treatment of Mental Health Disorders using Emotional Virtual Agents." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/64082.

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[EN] Interaction is defined as the realization of a reciprocal action between two or more people or things. Particularly in computer science, the term interaction refers to the discipline that studies the exchange of information between people and computers, and is generally known by the term Human-Computer Interaction (HCI). Good design decisions and an adequate development of the software is required for efficient HCI to facilitate the acceptability of computer-based applications by the users. In clinical settings it is essential to eliminate any barrier and facilitate the interaction betwe
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Fredette, Danielle Marie. "Fuel-Saving Behavior for Multi-Vehicle Systems: Analysis, Modeling, and Control." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500479676353809.

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Dao, Thanh-Son. "A Decentralized Approach to Dynamic Collaborative Driving Coordination." Thesis, 2008. http://hdl.handle.net/10012/3927.

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This thesis presents a novel approach to several problems in intelligent transportation systems using collaborative driving coordination. With inter-vehicle communication and intelligent vehicle cooperation, important tasks in transportation such as lane position determination, lane assignment and platoon formation can be solved. Several topics in regard to inter-vehicle communication, lane positioning, lane assignment and platoon formation are explored in this thesis: First, the design and experimental results of low-cost lane-level positioning system that can support a large number of trans
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Book chapters on the topic "Emotional intelligent agent driving"

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Frasson, Claude, Pierre Olivier Brosseau, and Thi Hong Dung Tran. "Virtual Environment for Monitoring Emotional Behaviour in Driving." In Intelligent Tutoring Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07221-0_10.

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Septseault, Cyril, and Alexis Nédélec. "A Model of an Embodied Emotional Agent." In Intelligent Virtual Agents. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550617_49.

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Nkambou, R., Y. Laporte, R. Yatchou, and G. Gouradères. "Embodied Emotional Agent in Intelligent Training System." In Recent Advances in Intelligent Paradigms and Applications. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1770-6_11.

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Scalco, Andrea. "Trait Emotional Intelligence: Modelling Individual Emotional Differences in Agent-Based Models." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19629-9_30.

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Park, Gi-Duck, Robert McCartney, and Jung-Jin Yang. "The Design of a Self-locating Automatic-Driving Robot." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_36.

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Kuremoto, Takashi, Tetsuya Tsurusaki, Kunikazu Kobayashi, Shingo Mabu, and Masanao Obayashi. "A Model of Emotional Intelligent Agent for Cooperative Goal Exploration." In Intelligent Computing Theories. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39479-9_3.

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de Menezes, Ilusca Lima Lopes, and Claude Frasson. "Using an Emotional Intelligent Agent to Reduce Resistance to Change." In Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69132-7_108.

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Bickmore, Timothy, Laila Bukhari, Laura Pfeifer Vardoulakis, Michael Paasche-Orlow, and Christopher Shanahan. "Hospital Buddy: A Persistent Emotional Support Companion Agent for Hospital Patients." In Intelligent Virtual Agents. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33197-8_56.

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Rincon, J. A., V. Julian, and C. Carrascosa. "An Emotional-Based Hybrid Application for Human-Agent Societies." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19719-7_18.

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Yang, Meng, Jianqiao Wang, Yuqi Xia, Fan Yang, and Xuemin Zhang. "Effects of Music’s Emotional Styles and Tempo on Driving Behavior and Eye Movement: A Driving Simulation Study." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37835-5_49.

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Conference papers on the topic "Emotional intelligent agent driving"

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Yan, Shengchao, Lukas König, and Wolfram Burgard. "Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801946.

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Chikaraddi, Ashok, Sanjana G. Janakki, Suvarna G. Kanakaraddi, and Praveenkumar S. M. "Emotion-Driven Music Recommender System with Deep Learning and Streamlit Integration." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894317.

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Yang, Ruoxuan, Xinyue Zhang, Anais Fernandez-Laaksonen, Xin Ding, and Jiangtao Gong. "Driving Style Alignment for LLM-powered Driver Agent." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802629.

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Kirubakaran, N., Nandhikaa Narayani G, Harsini S G, Chandrahasini S, and Sahana G. "A Data-Driven Approach to Habit Prediction: Computational Modeling and Emotion Analytics." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011893.

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Samiuddin, Jilan, Benoit Boulet, and Di Wu. "Trajectory Prediction for Autonomous Driving Using Agent-Interaction Graph Embedding." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920250.

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Hong, Shi Yin, Uttamasha Oyshi, Quan Mai, Gibson Nkhata, and Susan Gauch. "Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis." In 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2024. https://doi.org/10.1109/wi-iat62293.2024.00023.

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Pu, Yuxuan, Yang Li, Beihao Xia, Xiaowei Wang, Hongmao Qin, and Lei Zhu. "A Lightweight Lane-Guided Vector Transformer for Multi-Agent Trajectory Prediction in Autonomous Driving." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919701.

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Lai, Weichen, Suchuan Ma, Meng Yuan, Chi Zhang, and Jianyang Huang. "Integration of Emotional-Cognitive Architecture in ACT-R for Multi-Agent Social Dynamics for Disaster Response Simulation." In 2024 International Conference on Intelligent Robotics and Automatic Control (IRAC). IEEE, 2024. https://doi.org/10.1109/irac63143.2024.10871489.

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Jin, Ye, Ruoxuan Yang, Zhijie Yi, et al. "SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers’ Driving-thinking Data." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802229.

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Mao, Jian, Kailong Zhang, Qiang Yu, Jinfei Wu, Jiahao Zhang, and Arnaud De La Fortelle. "A Novel Cooperative Driving Mechanism Based on Multi-Agent Deep Reinforcement Learning with Priority Parameter Sharing and Safety Rules." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919192.

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Reports on the topic "Emotional intelligent agent driving"

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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