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.

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Table of Contents (5 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Understanding the Depth-First Search Algorithm
Understanding the Depth-First Search Algorithm
Installing and setting up libraries
Introduction to file searching applications
Basic search concepts
Formulating the search problem
Building trees with nodes
Stack data structure
The DFS algorithm
Do it yourself
Summary
2
Understanding the Breadth-First Search Algorithm
Understanding the Breadth-First Search Algorithm
Understanding the LinkedIn connection feature
Graph data structure
Queue data structure
The BFS algorithm
BFS versus DFS
Do it yourself
Summary
3
Understanding the Heuristic Search Algorithm
Understanding the Heuristic Search Algorithm
Revisiting the navigation application
The priority queue data structure
Visualizing a search tree
Greedy BFS
A* Search
What is a good heuristic function?
Summary
4
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Summary

In this chapter, to help you understand the BFS algorithm, we revisited the concepts of state and node. You learned about the graph and queue data structures, and we discussed the differences between the DFS and BFS algorithms.

In the next chapter, you'll be learning about the heuristic search algorithm. Instead of giving preference to child or sibling nodes, this method gives preference to the nodes that are closest to the goal state; the term heuristic refers to the measure of how close the nodes are to the goal state.

Please refer to the link https://www.packtpub.com/sites/default/files/downloads/HandsOnArtificialIntelligenceforSearch_ColorImages.pdf for the colored images of this chapter.
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