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.

Who this book is for

This book is for developers who are keen to get started with AI and develop practical AI-based applications. Developers who want to upgrade their normal applications to smart and intelligent versions will find this book useful. A basic knowledge and understanding of Python are assumed.

What this book covers

Chapter 1, Understanding the Depth-First Search Algorithm, practically explains the DFS algorithm with the help of a search tree. The chapter also delves into recursion, which eliminates the need to have an explicit stack.

Chapter 2, Understanding the Breadth-First Search Algorithm, teaches you how to traverse a graph layer-wise using a LinkedIn connection feature as an example.

Chapter 3, Understanding the Heuristic Search Algorithm, takes you through the priority queue data structure and explains how to visualize search trees. The chapter also covers problems related to greedy best-first search, and how A* solves that problem.

To get the most out of this book

The software requirements for running the codes are as follows:

• Python 2.7.6
• Pydot and Matplotlib libraries
• LiClipse

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Artificial-Intelligence-for-Search. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/HandsOnArtificialIntelligenceforSearch_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The State class has to be changed for every application, even though the search algorithm is the same."

A block of code is set as follows:

def checkGoalState(self):
        """
        This method checks whether the person is Jill.
        """ 
        #check if the person's name is Jill
        return self.name == "Jill"

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

#create a dictionary with all the mappings
connections = {}
connections["Dev"] = {"Ali", "Seth", "Tom"}
connections["Ali"] = {"Dev", "Seth", "Ram"}
connections["Seth"] = {"Ali", "Tom", "Harry"}
connections["Tom"] = {"Dev", "Seth", "Kai", 'Jill'}
connections["Ram"] = {"Ali", "Jill"}

Any command-line input or output is written as follows:

$ pip install pydot

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.

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