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Artificial Intelligence and Machine Learning Fundamentals
Artificial Intelligence and Machine Learning Fundamentals

Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances

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Artificial Intelligence and Machine Learning Fundamentals

Introduction


In the previous lesson, we understood the significance of an intelligent agent. We also examined the game states for a game AI. In this lesson, we will focus on how to create and introduce intelligence into an agent.

We will look at reducing the number of states in the state space and analyze the stages that a game board can undergo and make the environment work in such a way that we win. By the end of this lesson, we will have a Tic-Tac-Toe player who never loses a match.

Exercise 4: Teaching the Agent to Win

In this exercise, we will see how the steps needed to win can be reduced. We will be making the agent that we developed in the previous lesson detect situations where it can win a game. Compare the number of possible states to the random play as an example.

  1. We will be defining two functions, ai_move and all_moves_from_board. We will create ai_move so that it returns a move that will consider its own previous moves. If the game can be won in that move, ai_move will select...

Heuristics


In this topic, we will formalize informed search techniques by defining and applying heuristics to guide our search.

Uninformed and Informed Search

In the Tic-Tac-Toe example, we implemented a greedy algorithm that first focused on winning, and then focused on not losing. When it comes to winning the game immediately, the greedy algorithm is optimal, because there is never a better step than winning the game. When it comes to not losing, it matters how we avoid the loss. Our algorithm simply chose a random safe move without considering how many winning opportunities we have created.

Breadth First Search and Depth First Search are uninform, because they consider all possible states in the game. An informed search explores the space of available states intelligently.

Creating Heuristics

If we want to make better decisions, we apply heuristics to guide the search in the right direction by considering longer-term utility. This way, we can make a more informed decision in the present...

Pathfinding with the A* Algorithm


In the first two topics, we learned how to define an intelligent agent, and how to create a heuristic that guides the agent toward a desired state. We learned that this was not perfect, because at times we ignored a few winning states in favor of a few losing states.

We will now learn a structured and optimal approach so that we can execute a search for finding the shortest path between the current state and the goal state: the A* ("A star" instead of "A asterisk") algorithm:

Figure 2.3: Finding the shortest path in a maze

For a human, it is simple to find the shortest path, by merely looking at the image. We can conclude that there are two potential candidates for the shortest path: route one starts upwards, and route two starts to the left. However, the AI does not know about these options. In fact, the most logical first step for a computer player would be moving to the square denoted by the number 3 in the following diagram:

Why? Because this is the only...

Game AI with the Minmax Algorithm and Alpha-Beta Pruning


In the first two topics, we saw how hard it was to create a winning strategy for a simple game such as Tic-Tac-Toe. The last topic introduced a few structures for solving search problems with the A* algorithm. We also saw that tools such as the simpleai library help us reduce the effort we put in to describe a task with code.

We will use all of this knowledge to supercharge our game AI skills and solve more complex problems.

Search Algorithms for Turn-Based Multiplayer Games

Turn-based multiplayer games such as Tic-Tac-Toe are similar to pathfinding problems. We have an initial state, and we have a set of end states, where we win the game.

The challenge with turn-based multiplayer games is the combinatoric explosion of the opponent's possible moves. This difference justifies treating turn-based games differently than a regular pathfinding problem.

For instance, in the Tic-Tac-Toe game, from an empty board, we can select one of the nine...

Summary


In this lesson, we learned how to apply search techniques to play games.

First, we created a static approach that played the Tic-Tac-Toe game based on predefined rules without looking ahead. Then, we quantified these rules into a number we called heuristics. In the next topic, we learned how to use heuristics in the A* search algorithm to find an optimal solution to a problem.

Finally, we got to know the Minmax and the NegaMax algorithms so that the AI could win two-player games.

Now that you know the fundamentals of writing game AI, it is time to learn about a different field within artificial intelligence: machine learning. In the next lesson, you will learn about regression.

Summary

In this chapter, we learned how to apply search techniques to play games.

First, we created a static approach that played the Tic-Tac-Toe game based on predefined rules without looking ahead. Then, we quantified these rules into a number we called heuristics. In the next topic, we learned how to use heuristics in the A* search algorithm to find an optimal solution to a problem.

Finally, we got to know the Minmax and the NegaMax algorithms so that the AI could win two-player games.

Now that you know the fundamentals of writing game AI, it is time to learn about a different field within artificial intelligence: machine learning. In the next chapter, you will learn about regression.

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Key benefits

  • Practical examples that explain key machine learning algorithms
  • Explore neural networks in detail with interesting examples
  • Master core AI concepts with engaging activities

Description

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Who is this book for?

Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

What you will learn

  • Understand the importance, principles, and fields of AI
  • Implement basic artificial intelligence concepts with Python
  • Apply regression and classification concepts to real-world problems
  • Perform predictive analysis using decision trees and random forests
  • Carry out clustering using the k-means and mean shift algorithms
  • Understand the fundamentals of deep learning via practical examples

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 12, 2018
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781789801651
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Google
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Product Details

Publication date : Dec 12, 2018
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781789801651
Vendor :
Google
Category :
Languages :
Tools :

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Table of Contents

7 Chapters
Principles of Artificial Intelligence Chevron down icon Chevron up icon
AI with Search Techniques and Games Chevron down icon Chevron up icon
Regression Chevron down icon Chevron up icon
Classification Chevron down icon Chevron up icon
Using Trees for Predictive Analysis Chevron down icon Chevron up icon
Clustering Chevron down icon Chevron up icon
Deep Learning with Neural Networks Chevron down icon Chevron up icon

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(110 Ratings)
5 star 60%
4 star 21.8%
3 star 11.8%
2 star 1.8%
1 star 4.5%
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Sonko May 31, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Disclaimer: I got a promotional copy. It is a great book, and provides a great start in you are trying to get into this sort of thing.
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Chandrakesh Shukla Feb 05, 2023
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Monika Gupta Feb 05, 2023
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very good
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Ali Zulu Jan 13, 2023
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Ashok Kumar Goyal Jan 12, 2023
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