Multi-Agent Search under Uncertainty
暫譯: 不確定性下的多智能體搜尋
Matzliach, Barouch
- 出版商: Wiley
- 出版日期: 2026-08-26
- 售價: $5,970
- 貴賓價: 9.5 折 $5,671
- 語言: 英文
- 頁數: 128
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1394418450
- ISBN-13: 9781394418459
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相關分類:
Reinforcement
海外代購書籍(需單獨結帳)
商品描述
Plan optimal multi-robot search paths despite imperfect sensor information
When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by the researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation.
The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice.
Key topics include:
- Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditions
- Multi-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasks
- Deep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviors
- Algorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readings
- Theoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development
Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.
商品描述(中文翻譯)
規劃最佳的多機器人搜尋路徑，儘管感測器資訊不完美
當多個機器人必須在存在假陽性和假陰性檢測錯誤的情況下定位目標時，路徑規劃變得極其複雜。不確定性下的多代理搜尋 直接針對這一挑戰。這本書由擁有防禦系統、應用數學和機器學習等多方面專業知識的研究人員撰寫，提供了搜尋和篩選理論的理論基礎以及可供實際應用的現成算法。
本書涵蓋了在不完整或嘈雜資訊下運作的自主移動代理的合作搜尋和導航方法。讀者將學習如何通過試錯互動而非預先編程的指令，利用深度 Q-學習使機器人發展複雜行為。應用範圍包括搜尋和救援行動、軍事監視、環境監測和安全系統。隨書附帶的網站提供了用於模擬練習的 Python 代碼。
主要主題包括：
- 基於價值的 Q-學習方法，機器人在感測器不確定性條件下學習特定狀態下特定行動的預期獎勵
- 多代理強化學習方法，群體機器人協作學習以完成合作搜尋任務
- 使用神經網絡的深度強化學習，處理高維感測輸入並執行複雜的搜尋和追蹤行為
- 在最小化檢測時間的同時，尋找和追蹤靜止和移動目標的算法，儘管存在假陰性和假陽性讀數
- 對搜尋和篩選理論的理論貢獻，以及在自主機器人系統開發中驗證的實用算法
本書旨在為研究生和機器人學及強化學習的研究人員提供一個將高級理論與實際應用相結合的橋樑。專業開發人員在構建自主系統時，將發現經過實際機器人開發測試的算法。
作者簡介
Barouch Matzliach, PhD, is a Lecturer at Tel Aviv University's Faculty of Engineering and a technology consultant to defense industries. He has thirty years of experience in the development and production of advanced land combat systems and is a recipient of the Israel Defense Award. His research at the LAMBDA Laboratory focuses on Multi-Agent Reinforcement Learning and autonomous AI applications
Evgeny Kagan, PhD, CandSc-Eng, is a Senior Lecturer at the Department of Industrial Engineering at Ariel University and a research fellow at LAMBDA laboratory. With over thirty years of experience in applied mathematics and engineering, he has authored more than eighty scientific publications including four books.
Irad Ben-Gal, PhD, Prof., is a Full Professor at the Faculty of Engineering at Tel Aviv University and a head of LAMBDA laboratory at Tel Aviv University and a world-renowned expert in data science, AI, and machine learning with over twenty-five years of academic and practical experience. He co-heads the TAU/Stanford University Digital Living 2030 initiative, published four books, more than 150 scientific papers and patents and has collaborated with Oracle, Intel, GM, AT&T, Applied Materials, Siemens, Kimberly Clark and Nokia.
作者簡介(中文翻譯)
Barouch Matzliach, PhD, 是特拉維夫大學工程學院的講師，並擔任國防產業的技術顧問。他在先進陸地作戰系統的開發和生產方面擁有三十年的經驗，並獲得以色列國防獎。他在LAMBDA實驗室的研究專注於多智能體強化學習和自主人工智慧應用。
Evgeny Kagan, PhD, CandSc-Eng, 是阿里爾大學工業工程系的高級講師，也是LAMBDA實驗室的研究員。他在應用數學和工程領域擁有超過三十年的經驗，並撰寫了超過八十篇科學出版物，包括四本書籍。
Irad Ben-Gal, PhD, Prof., 是特拉維夫大學工程學院的正教授，並擔任LAMBDA實驗室的主任，是數據科學、人工智慧和機器學習的世界知名專家，擁有超過二十五年的學術和實務經驗。他共同負責TAU/史丹佛大學的數位生活2030計畫，出版了四本書籍，發表了超過150篇科學論文和專利，並與Oracle、Intel、GM、AT&T、Applied Materials、Siemens、Kimberly Clark和Nokia等公司合作。
