Books

What happens when intelligent agents must negotiate, cooperate, compete, and learn simultaneously?Welcome to the advanced frontier of Artificial Intelligence.Game Theory and Artificial Intelligence (VOL-2) explores how modern AI systems make strategic decisions in environments filled with uncertainty, multiple objectives, competing interests, and dynamic interactions.Inside this volume, readers will discover:✓ Nash Q-Learning and Strategic Reinforcement Learning✓ Deep Multi-Agent Reinforcement Learning (MARL)✓ DQN, Actor-Critic, MADDPG, QMIX, and CTDE Frameworks✓ Mechanism Design and Incentive Engineering✓ Decision Making Under Uncertainty✓ Social Choice Theory and Collective Intelligence✓ Game-Theoretic Machine Learning✓ Robotics, Cybersecurity, Economics, and Communication Networks✓ Evolutionary Game Theory and Learning Dynamics✓ Quantum Game Theory and Future AI Research✓ AI Safety, Alignment, and GovernanceFrom autonomous vehicles and intelligent robots to cybersecurity defense systems and large-scale AI simulations, this book provides the theoretical and practical foundations necessary to understand the future of strategic artificial intelligence.Whether you are a student, researcher, engineer, or AI professional, this volume offers a roadmap to some of the most exciting and influential areas of modern AI research.

Every intelligent system faces a fundamental challenge:How do you find the best solution when there are millions—or even trillions—of possibilities?From A* search and heuristic optimization to neural architecture search, hyperparameter tuning, constraint satisfaction, and quantum optimization, modern AI depends on sophisticated strategies for navigating combinatorial search spaces.In this advanced second volume, Anshuman Mishra explores the powerful algorithms that enable intelligent systems to search smarter, optimize faster, and scale beyond brute force computation.Discover how combinatorial thinking drives machine learning, optimization, quantum AI, and the future of intelligent decision-making.The future of AI belongs to those who understand combinatorial complexity.

Artificial Intelligence is not merely about data, learning, or neural networks—it is fundamentally about exploring vast spaces of possibilities.Every search algorithm, planning system, optimization framework, recommendation engine, and machine learning model faces a common challenge: navigating an enormous combinatorial universe of potential solutions.In Combinatorial Thinking in Artificial Intelligence, Anshuman Mishra reveals the mathematical foundations that drive modern AI systems. Through permutations, combinations, graph theory, probability, search strategies, and optimization techniques, readers learn how intelligent systems reason, search, and make decisions in complex environments.If you want to understand why AI algorithms work—not just how to implement them—this book provides the missing mathematical perspective.Discover the combinatorial engine behind intelligence.

Explore the advanced frontier of Category-Theoretic Artificial Intelligence. Discover how transformers, symbolic reasoning, reinforcement learning, higher categories, topos theory, and compositional learning can be unified through the powerful language of category theory. An essential guide for researchers, AI professionals, and future AGI innovators.

WHO SHOULD READ THIS BOOK?This book is ideal for:·        BCA, MCA, B.Tech, M.Tech students·        UGC NET aspirants·        AI/ML researchers·        Data scientists·        AI developers·        University professors·        PhD scholars·        Industry professionals working with black-box models·        Anyone who wants mathematical clarity on XAIIts writing style balances mathematical rigor with readability, making it useful for self-study and classroom use. TEACHING & LEARNING BENEFITS·        50+ diagrams, proofs, and mathematical derivations.·        Step-by-step logical flow for each model.·        Case studies from healthcare, finance, law, and engineering.·        Practical coding references (without over-reliance on tools).·        Integration of statistics, calculus, causality, and deep learning.·        Real-world examples for intuitive understanding.·        Problems at the end of each chapter (optional addition).Instructors can adopt this book for academic courses in:·        Explainable AI·        Machine Learning·        Statistical Inference·        Causality·        Artificial Intelligence Foundations·        Deep Learning Interpretability UNIQUE CONTRIBUTIONS OF THIS BOOKUnlike other XAI or ML books, this work by Anshuman Mishra offers:·        Mathematical derivations for SHAP, IG, LIME, and other explainability tools.·        Original proofs for fairness properties in attribution methods.·        Detailed causal diagrams and do-calculus explanations.·        A structured approach to XAI evaluation metrics.·        Coverage of transformer explainability—rare in academic books.·        Clarity in blending classical mathematical theory with modern AI systems.This makes the book a reference-level resource for the next decade of AI learning.

Geopolitics is too late. Discover how the Naciro Engine uses AI to measure the pulse of nations in real-time, providing a 15-minute recalibration of global truth and power. Check the live data on https://nationfiles.com

A practical guide to Claude Code, written with Claude Code.Over 300 pages built on the official Anthropic documentation and verified on Claude Code v2.1.311: from installation to Plan Mode, CLAUDE.md, context management, permissions, Skills, subagents, hooks, MCP and plugins, with real examples on WordPress and Node.js instead of slide-deck demos. It opens with a foreword written by Claude Opus, which read the manuscript and commented on it candidly.The book is free: pay-what-you-want with a $0 minimum, you decide whether and how much to contribute. The Creative Commons BY-SA 4.0 license lets you redistribute and modify it, as long as you credit the source and keep the same license.

ဥပမာအားဖြင့်၊ သင်က AI ကို "မိုးရွာရင် ထီးယူသွားပါ" ဟု ခိုင်းထားလျှင်၊ မိုးသည်းထန်စွာ ရွာနေသော်လည်း အိမ်ခေါင်မိုး ပြိုကျနေပါက ၎င်းသည် ထီးကိုသာ ကိုင်၍ အိမ်ထဲတွင် ငုတ်တုတ်ထိုင်နေပေလိမ့်မည်။ အဘယ်ကြောင့်ဆိုသော် "အိမ်ခေါင်မိုး ပြိုလျှင် ပြေးပါ" ဟူသော စည်းမျဉ်းကို သင်က ထည့်မပေးထား သောကြောင့် ဖြစ်သည်။

This book gives senior technology leaders a practical operating system for enterprise AI strategy. It turns scattered pilots into a board-ready plan by walking through strategy, economics, vendor decisions, ownership, governance, GenAI architecture, and roadmap assembly: AI Platform Scorecard, Use-Case Prioritization Matrix, Budget Model + Cost Guardrails, Vendor Decision Framework, RACI Matrix.

Installing safety hooks for Claude Code takes about thirty seconds. Proving they actually refuse anything is the step almost everyone skips — and an unverified guard looks exactly like a working one. Free: the pre-flight checklist, which event can refuse a tool call and which only watches, how to make a guard fire on purpose, what a broken hook does, and a catalogue of all 916 example hooks in cc-safe-setup.

This book is the summary of everything you are going to need to learn how to deploy LLM to production. All the tips and tricks, and some interview questions.

A working engineer's guide to building and maintaining production software with autonomous terminal coding agents (382 manuscript pages).

Threat → Control → Requirement → Verification. The field manual to read before you ship an autonomous agent.

AI is basically a very well-optimized math system pretending to sound like a person. That's it. That's the whole secret.This book explains exactly how, no math, no code, no jargon. Just a straight conversation about what's actually happening inside the machine everyone won't stop talking about.

Your agent passed every load test and timed out in production anyway. This free practitioner's guide shows why the traditional performance playbook breaks for agentic AI, and what replaces it: latency budgets, bounded autonomy, token SLOs, and production-grade observability, from a performance engineering and observability architect with two decades in production.