Algorithm Recipes Based On Game Theory: For AI Models by Richard Aragon
English | July 26, 2024 | ISBN: N/A | ASIN: B0DBFGZ5SN | 124 pages | EPUB | 1.62 Mb
Unlock the Power of Game Theory in AI!
Discover the revolutionary approach to artificial intelligence with "
Algorithm Recipes Based On Game Theory: For AI Models". Written by renowned AI expert Richard Aragon, this book delves into the fascinating intersection of game theory and AI, offering a collection of innovative algorithms designed to solve complex problems.
Why You Should Read This Book:
* Innovative Approach: Explore how principles from game theory can inspire and enhance AI algorithms, leading to more robust, fair, and efficient solutions.
* Comprehensive Coverage: Each chapter presents a unique "recipe" with a use case, mathematical foundation, ingredients, preparation instructions, deployment advice, code implementation, and a summary of the algorithm's novelty and usability.
* Practical Applications: Learn how to apply these cutting-edge algorithms to real-world problems, including multi-agent systems, feature selection, optimization, anomaly detection, and more.
* Expert Insights: Benefit from Richard Aragon's deep expertise and practical experience in AI and game theory.
Inside the Book:
* Chapter 1: The Nash Equilibrium Optimizer (NEO)
* Chapter 2: The Minimax Classifier (MMC)
* Chapter 3: The Cooperative Multi-Agent Reinforcement Learner (CMARL)
* Chapter 4: The Shapley Value Feature Selector (SVFS)
* Chapter 5: The Stackelberg Game Recommender System (SGRS)
* Chapter 6: The Evolutionary Stable Strategy Neural Network (ESSNN)
* Chapter 7: The Zero-Sum Game Neural Network Trainer (ZSGNNT)
* Chapter 8: The Pareto Optimal Multi-Objective Optimizer (POMOO)
* Chapter 9: The Bayesian Game Anomaly Detector (BGAD)
* Chapter 10: The Cournot Competition Regression Model (CCRM)
* Chapter 11: The Evolutionary Game Theory-Based Genetic Algorithm (EGTGA)
* Chapter 12: The Cooperative Markov Decision Process (CMDP)
* Chapter 13: The Mixed Strategy Nash Equilibrium Optimizer (MSNEO)
* Chapter 14: The Cooperative Bargaining Agreement Clustering (CBAC)
* Chapter 15: The Shapley Value-Based Fair Feature Selection (SVFFS)
Who Should Read This Book:
* AI Researchers and Practitioners: Enhance your toolkit with game theory-inspired algorithms.
* Data Scientists: Discover innovative methods for model development and optimization.
* Students and Educators: Gain a comprehensive understanding of the intersection of game theory and AI.
* Developers: Access practical, ready-to-implement algorithms for real-world applications.
Why Choose This Book?
"
Algorithm Recipes Based On Game Theory: For AI Models" is not just a book; it's a practical guide that bridges the gap between theoretical concepts and real-world applications. Whether you're an AI professional, a data scientist, or a student, this book provides the insights and tools needed to excel in the rapidly evolving field of artificial intelligence.
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