Architectures for Agentic AI: Integrating Multi-Agent Systems, Reinforcement Learning, and Llms for Autonomous Decision-Making
暫譯: 代理人工智慧架構：整合多代理系統、強化學習與大型語言模型以實現自主決策

Oliveira, Pedro, Da Cruz Pereira, João, Novais, Paulo

Architectures for Agentic AI: Integrating Multi-Agent Systems, Reinforcement Learning, and Llms for Autonomous Decision-Making
  • 出版商: Springer
  • 出版日期: 2026-05-19
  • 售價: $2,430
  • 貴賓價: 9.8 折 $2,381
  • 語言: 英文
  • 頁數: 94
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3032247802
  • ISBN-13: 9783032247803
  • 相關分類: Reinforcement

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

本書探討了新興的代理人工智慧（Agentic AI）範式，該範式中大型語言模型（Large Language Models, LLMs）與強化學習（Reinforcement Learning, RL）相互融合，創造出智能、自主且具適應性的系統。它提供了一個統一的理論基礎，並將其與實際實施相連接，為讀者提供了一條從概念到執行的清晰路徑。本書還將提供代理人工智慧、大型語言模型和強化學習的整合性方法。儘管這些主題通常是分開研究的，但本書提供了一個連貫的框架，將它們統一起來，填補了人工智慧理論、系統設計和實際應用之間的關鍵空白。

在快速發展的人工智慧技術時代，理解代理人工智慧系統的運作方式以及它們與傳統人工智慧的不同之處至關重要。本書指導研究人員、工程師和人工智慧從業者了解賦予代理推理、合作和從反饋中學習的架構原則。它進一步展示了如何利用強化學習來微調大型語言模型，以產生更具針對性和上下文感知的輸出，增強它們在多代理協作和自主決策中的角色。

內容從人工智慧的演變到代理人工智慧，涵蓋了架構設計、學習機制以及大型語言模型和強化學習的整合策略。一個真實案例研究將理論與實踐相結合，說明這些技術如何結合以構建可解釋的系統。讀者將發現自適應編排策略、增強模型可解釋性的方法，以及開發智能代理生態系統的設計模板。到最後，讀者不僅能理解代理人工智慧的內部運作，還能獲得設計和實施自己基於代理的框架的工具。建議具備Python的基本知識，以便充分參與實踐方面的內容。

作者簡介

Dr. Pedro Oliveira obtained his PhD from the School of Engineering of the University of Minho, Braga, Portugal, and works as a researcher at the ALGORITMI Centre, in the Synthetic Intelligence Lab (ISLAB) research group. He holds a MSc degree in Informatics Engineering from the same university, where from his position as Invited Professor he teaches classes on Multi-Agent Systems, Data Engineering and applications of Machine and Deep Learning models. His research interests include Recurrent Neural Networks, where models such as Long Short-Term Memory or Gated Recurrent Unit are applied in the context of Time Series Problems. He also uses these to detect anomalies in different systems applied in the same context as before. Recently, he has started to investigate frameworks that incorporate Multi-Agent Systems connected to Reinforcement Learning, particularly Agentic AI.

João da Cruz Pereira is a MSc student in Computer Engineering at the School of Engineering of the University of Minho, Braga, Portugal. He is also a scholarship holder at the ALGORITMI Centre, in the Synthetic Intelligence Lab (ISLAB) research group. He is employed at the same University as an Invited Professor, teaching classes on Artificial Intelligence, particularly on Symbolic AI, Data Science and the use of Machine and Deep Learning models. The focus of his interest areas is Image Classification, namely the classification of Water Quality by using Remote Sensing techniques such as Satellite Imagery, as well as the inclusion of Large Language Models to introduce interpretability to the different frameworks created to help in decision making.

Paulo Novais is a Full Professor of Computer Science at the Department of Informatics, the School of Engineering, the University of Minho (Portugal) and a researcher at the ALGORITMI Centre, where he leads the research group Synthetic Intelligence Lab, and coordinates the Computer Science and Technology (CST) research line. He is the director of the PhD Program in Informatics and co-founder and Deputy Director of the Master in Law and Informatics at the University of Minho. He started his career developing scientific research in the field of Intelligent Systems/Artificial Intelligence (AI), namely in Knowledge Representation and Reasoning, Machine Learning and Multi-Agent Systems. His interest in the last years was absorbed by the different, yet closely related, concepts of Ambient Intelligence/Ambient Assisted Living, Conflict Resolution, Behavioral Analysis, Intelligent Tutors and the incorporation of AI methods and techniques in these fields. His main research aim is to make systems a little smarter, intelligent and also reliable. He is the co-author of over 450 book chapters, journal papers, conference and workshop papers and books. He was President of General Assembly and Former President of APPIA (the Portuguese Association for Artificial Intelligence) between 2016 and 2019, Senior member of the IEEE (Institute of Electrical and Electronics Engineers), member of the IFIP (International Federation for Information Processing) - TC 12 Artificial Intelligence and of the executive committee of the IBERAMIA (IberoAmerican Society of Artificial Intelligence), and is the Coordinator of the Scientific Committee of the Gulbenkian Scholarship Program "New Talent in Artificial Intelligence" of the Calouste Gulbenkian Foundation. He has served as an expert of several institutions such as the EU Commission, FCT (Portuguese agency that supports science, technology and innovation), A3ES (Agency for Assessment and Accreditation of Higher Education) and ANI (National Innovation Agency).

作者簡介(中文翻譯)

佩德羅·奧利維拉博士於葡萄牙布拉加的米尼奧大學工程學院獲得博士學位，並在ALGORITMI中心的合成智慧實驗室（ISLAB）研究小組擔任研究員。他擁有同一所大學的資訊工程碩士學位，並以受邀教授的身份教授多代理系統、資料工程以及機器學習和深度學習模型的應用課程。他的研究興趣包括循環神經網絡，特別是在時間序列問題中應用長短期記憶（Long Short-Term Memory）或門控循環單元（Gated Recurrent Unit）等模型。他還利用這些模型來檢測在相同背景下不同系統中的異常。最近，他開始研究將多代理系統與強化學習相結合的框架，特別是代理人工智慧（Agentic AI）。

若昂·達·克魯茲·佩雷拉是葡萄牙布拉加的米尼奧大學工程學院的計算機工程碩士生。他同時也是ALGORITMI中心合成智慧實驗室（ISLAB）研究小組的獎學金持有者。他在同一所大學擔任受邀教授，教授人工智慧課程，特別是符號人工智慧、資料科學以及機器學習和深度學習模型的使用。他的興趣領域集中在影像分類，特別是利用遙感技術（如衛星影像）對水質進行分類，以及引入大型語言模型以提高不同框架在決策過程中的可解釋性。

保羅·諾瓦伊斯是葡萄牙米尼奧大學資訊系的計算機科學全職教授，並在ALGORITMI中心擔任研究員，領導合成智慧實驗室的研究小組，並協調計算機科學與技術（CST）研究方向。他是資訊學博士課程的主任，也是米尼奧大學法律與資訊碩士課程的共同創辦人及副主任。他的職業生涯始於智能系統/人工智慧（AI）領域的科學研究，特別是在知識表示與推理、機器學習和多代理系統方面。近年來，他對環境智慧/環境輔助生活、衝突解決、行為分析、智能導師以及在這些領域中融入AI方法和技術等不同但密切相關的概念產生了濃厚的興趣。他的主要研究目標是使系統變得更智能、可靠。他是450多篇書籍章節、期刊論文、會議和研討會論文及書籍的共同作者。他曾於2016年至2019年擔任APPIA（葡萄牙人工智慧協會）大會主席及前主席，並是IEEE（電氣和電子工程師學會）的資深會員，IFIP（國際資訊處理聯合會）- TC 12人工智慧的成員，以及IBERAMIA（伊比利亞美洲人工智慧學會）執行委員會的成員，並擔任卡洛斯·古爾本基安基金會「人工智慧新人才」獎學金計畫的科學委員會協調員。他曾擔任多個機構的專家，如歐盟委員會、FCT（支持科學、技術和創新的葡萄牙機構）、A3ES（高等教育評估與認證機構）和ANI（國家創新機構）。

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