ISBN: 9783032247803 - architectures for agentic ai: integrating multi-agent systems, reinforcement learning, and llms for autonomous decision-making (springerbriefs in intelligent systems) (18 resultados)

Paperback. Condición: New. This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects.…

Paperback. Condición: new. Paperback. This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects. mso-bidi-font-weight: bold;">In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Condición: New.

Paperback. Condición: Brand New. 109 pages. 6.14x0.23x9.21 inches. In Stock.

Condición: NEW.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application.In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making.The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects.…

Condición: New.

Taschenbuch. Condición: Neu. Architectures for Agentic AI | Integrating Multi-Agent Systems, Reinforcement Learning, and LLMs for Autonomous Decision-Making | Pedro Oliveira (u. a.) | Taschenbuch | SpringerBriefs in Intelligent Systems | xv | Englisch | 2026 | Springer | EAN 9783032247803 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: new. Paperback. This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects. mso-bidi-font-weight: bold;">In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: New. This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects.…

Condición: new. Questo è un articolo print on demand.

Paperback. Condición: Brand New. 109 pages. 6.14x0.23x9.21 inches. In Stock. This item is printed on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application.In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making.The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects. 94 pp. Englisch.…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores the emerging paradigm of Agentic AI, where Large Language Models (LLMs) and Reinforcement Learning (RL) converge to create intelligent, autonomous, and adaptive systems. It provides a unified theoretical foundation and connects it to practical implementation, offering readers a clear path from concept to execution. It will also provide an integrative approach of Agentic AI, Large Language Models, and Reinforcement Learning. While these topics are often studied separately, this book provides a coherent framework that unites them, filling a critical gap between AI theory, system design, and real-world application. In an era of rapidly evolving AI technologies, understanding how Agentic AI systems operate, and how they differ from traditional AI, is essential. This book guides researchers, engineers, and AI practitioners through the architectural principles that empower agents to reason, cooperate, and learn from feedback. It further demonstrates how RL can fine-tune LLMs to produce more focused, context-aware outputs, strengthening their role in multi-agent collaboration and autonomous decision-making. The content unfolds from the evolution of AI to Agentic AI, covering architectural design, learning mechanisms, and integration strategies for LLMs and RL. A real-world case study anchors the theory in practice, illustrating how these technologies can be combined to build interpretable systems. Readers will discover adaptive orchestration strategies, methods for enhancing model interpretability, and design templates for developing intelligent agent ecosystems. By the end, readers will not only understand the inner workings of Agentic AI but also gain the tools to design and implement their own agent-based frameworks. A working knowledge of Python is recommended to fully engage with the practical aspects.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 112 pp. Englisch.…