Game Theory and Artificial Intelligence: Foundations, Multi-Agent Systems, Reinforcement Learning, and Intelligent Decision-Making (Vol. 2)

Subtitle

A Comprehensive Guide for Students, Researchers, and Professionals

Author: Anshuman Mishra

Artificial Intelligence has entered an era where intelligent systems are no longer isolated problem solvers. Autonomous agents negotiate, cooperate, compete, coordinate, and learn continuously in dynamic environments shared with humans and other intelligent machines. As AI expands into autonomous vehicles, robotics, smart cities, cybersecurity, finance, communication networks, and large-scale distributed systems, the need for strategic reasoning has become more critical than ever.

Game Theory and Artificial Intelligence: Foundations, Multi-Agent Systems, Reinforcement Learning, and Intelligent Decision-Making (Vol. 2) continues the journey begun in Volume 1 by exploring the most advanced developments at the intersection of Game Theory, Artificial Intelligence, Deep Reinforcement Learning, and Multi-Agent Systems. It provides readers with a comprehensive understanding of how intelligent agents make strategic decisions, cooperate under uncertainty, compete in adversarial environments, and learn optimal behaviors through interaction.

This volume is designed for undergraduate and postgraduate students, researchers, academicians, AI practitioners, robotics engineers, data scientists, economists, operations researchers, and professionals seeking to master the mathematical and computational foundations of strategic artificial intelligence. The topics covered reflect the latest research directions shaping the future of AI, including game-theoretic reinforcement learning, mechanism design, AI governance, evolutionary learning, quantum game theory, and large-scale multi-agent intelligence.

Purpose and Vision of the Book

The primary goal of this volume is to demonstrate how advanced game-theoretic concepts enable the development of intelligent systems capable of learning, adapting, negotiating, coordinating, and making robust decisions in complex environments.

Unlike traditional books that separately discuss game theory, reinforcement learning, or distributed AI, this volume integrates these disciplines into a unified framework that reflects how modern AI systems are actually designed and deployed.

The book is built around five guiding principles:

Build advanced strategic reasoning skills.
Integrate deep reinforcement learning with game theory.
Explain multi-agent intelligence using practical examples.
Bridge academic theory with industrial AI applications.
Prepare readers for cutting-edge research and innovation.

What This Volume Covers

This second volume extends the foundations established in Volume 1 into advanced research and real-world implementation. Readers will explore:

Game-Theoretic Reinforcement Learning
Multi-Agent Deep Reinforcement Learning
Decision Making Under Uncertainty
Mechanism Design
Social Choice Theory
AI Strategy and Competitive Learning
Robotics and Autonomous Systems
Communication Networks
Cybersecurity Games
Economic Strategy
Evolutionary Game Theory
Quantum Game Theory
AI Safety and Alignment
Multi-Agent Large Language Models
Emerging Research Directions

Each topic combines rigorous mathematical concepts with modern AI applications, making the material suitable for both academic study and professional practice.

Key Features of the Book

1. Advanced Multi-Agent Reinforcement Learning

Readers gain a deep understanding of modern multi-agent learning algorithms, including:

Nash Q-Learning
Correlated-Q Learning
Minimax-Q
Gradient-Based Game Learning
Equilibrium Learning
Multi-Agent Credit Assignment
Deep Q Networks (DQN)
Actor–Critic Architectures
MADDPG
QMIX
VDN
Centralized Training with Decentralized Execution (CTDE)

These algorithms represent the state of the art in collaborative and competitive AI research.

2. Intelligent Decision-Making Under Uncertainty

Real-world AI systems rarely operate in perfectly predictable environments.

This volume introduces mathematical models for reasoning under uncertainty through:

Expected Utility Theory
Prospect Theory
Risk Analysis
Ambiguity Modeling
Robust Decision-Making
Adaptive Strategies
Stochastic Optimization

Readers learn how intelligent agents make rational decisions when information is incomplete or constantly changing.

3. Mechanism Design and Incentive Engineering

Modern AI increasingly depends on designing systems where independent agents behave cooperatively while pursuing their own objectives.

The book explains:

Auction Theory
Vickrey Auctions
Combinatorial Auctions
Advertisement Auctions
Incentive Alignment
Contract Design
Resource Allocation
Mechanism Design Principles

These concepts are essential in digital marketplaces, blockchain systems, federated learning, cloud computing, and autonomous economic agents.

4. AI in Real-World Strategic Systems

One of the strengths of this volume is its strong emphasis on practical applications.

Readers discover how Game Theory powers intelligent systems in:

Autonomous robotics
Multi-robot coordination
Swarm intelligence
Self-driving vehicles
Cybersecurity defense
Wireless communication
Network optimization
Smart transportation
Financial trading
Supply chain optimization
Market competition
Distributed AI systems

Each application demonstrates how theoretical concepts become practical engineering solutions.

5. Evolutionary Learning and Future AI

The final chapters explore emerging research areas shaping the future of artificial intelligence.

Topics include:

Evolutionary Game Theory
Replicator Dynamics
Population-Based Training
Quantum Game Theory
AI Alignment
Multi-Agent Safety
Reward Hacking
Large Language Models (LLMs)
Hierarchical Multi-Agent Reinforcement Learning
Simulation-to-Reality Transfer
Emergent Cooperation
Future Open Research Problems

These chapters provide valuable insights for postgraduate students, PhD scholars, and AI researchers interested in the next generation of intelligent systems.

Who Should Read This Book?

This book is ideal for:

BCA students
MCA students
B.Tech students
M.Tech students
Computer Science researchers
Artificial Intelligence researchers
Machine Learning engineers
Robotics engineers
Data Scientists
Operations Research professionals
Cybersecurity researchers
Economists
AI developers
Autonomous Systems engineers
Faculty members
PhD scholars
Competitive examination aspirants

No prior expertise in advanced game-theoretic reinforcement learning is required, as the material is presented progressively with clear explanations and practical examples.

Why This Book Is Unique

Unlike many specialized texts that focus exclusively on game theory or machine learning, this book presents a unified framework that connects mathematics, artificial intelligence, strategic optimization, and multi-agent decision-making.

Key strengths include:

Comprehensive integration of Game Theory and AI
Modern reinforcement learning algorithms
Multi-agent system architectures
Decision-making under uncertainty
Mechanism design and incentive engineering
AI ethics and governance
Industry-oriented case studies
Research-focused discussions
Clear mathematical explanations
Practical AI applications

The writing style balances academic rigor with accessibility, making complex concepts understandable without sacrificing technical depth.

Applications Covered

The concepts discussed in this volume have applications across numerous domains, including:

Artificial Intelligence
Machine Learning
Deep Reinforcement Learning
Multi-Agent Systems
Robotics
Autonomous Vehicles
Cybersecurity
Communication Networks
Wireless Systems
Internet of Things (IoT)
Smart Cities
Cloud Computing
Blockchain
Digital Markets
Algorithmic Trading
Healthcare AI
Logistics and Supply Chains
Defense Systems
Recommendation Systems
Distributed Computing
Human–AI Collaboration

About the Author

Anshuman Mishra is an experienced academician, educator, researcher, and author specializing in Computer Science, Artificial Intelligence, Machine Learning, Mathematical Foundations of AI, Multi-Agent Systems, and Emerging Technologies. Through extensive teaching, curriculum development, and research, he has consistently focused on making advanced computational concepts accessible, practical, and relevant for students, professionals, and researchers.

His writing combines strong mathematical foundations with real-world implementation, enabling readers to understand both the theory and practical applications of modern AI technologies.

Conclusion

The future of Artificial Intelligence will be shaped not only by better algorithms but also by smarter strategic reasoning. As intelligent systems increasingly interact with one another in collaborative and competitive environments, Game Theory will remain one of the most important mathematical foundations of AI.

Game Theory and Artificial Intelligence: Foundations, Multi-Agent Systems, Reinforcement Learning, and Intelligent Decision-Making (Vol. 2) equips readers with the advanced knowledge, computational techniques, and strategic insights needed to understand, design, and implement the next generation of intelligent autonomous systems.

Whether you are preparing for academic research, industry innovation, competitive examinations, or advanced AI development, this book serves as a complete guide to mastering strategic intelligence in modern artificial intelligence.

Spectrum Allocation

Game Theory and Artificial Intelligence: Foundations, Multi-Agent Systems, Reinforcement Learning, and Intelligent Decision-Making -VOL2