Multi-Agent Technologies and Machine Learning / edited by Igor A. Sheremet.

Format:

Book

Contributor:

Sheremet, Igor A., editor.

Series:

Artificial intelligence (IntechOpen (Firm)) ; v.4

Artificial intelligence ; Volume 14

Language:

English

Subjects (All):

Artificial intelligence.

Multiagent systems.

Physical Description:

1 online resource (xiii, 132 pages) : illustrations.

Place of Publication:

London : IntechOpen, 2023.

Summary:

This book discusses multi-agent technologies (MATs) and machine learning (ML). These tools can be integrated and applied in industry, commerce, energy, medicine, psychology, and other areas. This volume consists of six chapters in three sections that discuss the integration, applications, and advanced results of MATs and ML.

Contents:

Preface

Section 1 Advanced Results in the Area of Integration of Multi-Agent Technologies and Machine Learning

Chapter 1 A State-of-the-Art Survey on Various Domains of Multi-Agent Systems and Machine Learning by Aida Huerta Barrientos and Alejandro Nila Luevano

Chapter 2 Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge by Theocharis Kravaris and George A. Vouros

Section 2 Applications of Multi-Agent Technologies Combined with Machine Learning

Chapter 3 Role of an Optimal Multiagent Scheduling in Different Applications Using ML by Fahmina Taranum, Sridevi K, Maniza Hijab, Syeda Fouzia Sayeedunissa, Afshan Kaleem and Niraja K.S.

Chapter 4 On an Approach to Knowledge Management and the Development of the Knowledge-Based Multi-Agent System by Evgeniy Zaytsev and Elena Nurmatova

Section 3 Advanced Developments in Multi-Agent Technologies and Machine Learning Creating Potential for Their Further Integration 71

Chapter 5 Modeling Electric Vehicle Charging Station Behavior Using Multiagent System by Jaslin Shaleem Khan, Malligama Arachchige Uditha Sudheera Navaratne and Janaka Bandara Ekanayake

Chapter 6 Approximate Dynamic Programming: An Efficient Machine Learning Algorithm.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.