Book Image

Hands-On Artificial Intelligence for Search

By : Devangini Patel
Book Image

Hands-On Artificial Intelligence for Search

By: Devangini Patel

Overview of this book

With the emergence of big data and modern technologies, AI has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, finance, and more. In this book, you will understand what artificial intelligence is. It explains in detail basic search methods: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems. But these are not optimal in either space or time and efficient approaches in time and space will be explored. We will also understand how to formulate a problem, which involves looking at it and identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms as they form the basis of search exploration. Finally, we will look into what a heuristic is as this decides the quality of one sub-solution over another and helps you decide which step to take.
Table of Contents (5 chapters)

Preface

With the emergence of big data and modern technologies, artificial intelligence (AI) has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, and finance.
This book will give you an understanding of what AI is. It explains basic search methods in detail: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems, but they are not optimal in terms of either space or time, and efficient approaches to space and time will be explored. We will also look at how to formulate a problem, which involves identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms, because they form the basis of search exploration. Finally, we will look into what a heuristic is, because this decides the suitability of one sub-solution over another and helps you decide which step to take.