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Hands-On Artificial Intelligence with Java for Beginners
Hands-On Artificial Intelligence with Java for Beginners

Hands-On Artificial Intelligence with Java for Beginners: Build intelligent apps using machine learning and deep learning with Deeplearning4j

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Hands-On Artificial Intelligence with Java for Beginners

Exploring Search Algorithms

In this chapter, we'll look at how to perform a search, and we will cover the basic requirements of implementing a search algorithm. We'll then practice by implementing Dijkstra's algorithm, before moving on to heuristics, showing how they can be used in search algorithms to improve the accuracy of search results.

In particular, we'll be focusing on the following topics:

  • An introduction to searching
  • Implementing Dijkstra's search
  • Understanding the notion of heuristics
  • A brief introduction to the A* algorithm
  • Implementing an A* algorithm

An introduction to searching

Let's look at what it means to search. If we want to apply a search to any problem, we will need four pieces of input, which are referred to as the state space, and are as follows:

[S, s, O, G]

The preceding types of input can be described as follows:

  • S: A set of implicitly given states—all of the states that might be explored in a search process.
  • s: The start symbol—the starting point of the search.
  • O: The state transition operators, which indicate how a search should proceed from one node to the next and what transitions are available to the search. This is an exhaustive list. Therefore, the state transition operator keeps track of these exhaustive lists.
  • G: The goal node, pointing to where the search should end.

With the preceding information, we can find the following values:

  • The minimum cost transaction for a goal state
  • ...

Implementing Dijkstra's search

Now, we will look at the code for Dijkstra's search algorithm, which we discussed in the An introduction to searching section.

Let's get straight to the code, and look at how it works. In the previous section, the very first thing that we showed were the vertices; each vertex had certain properties. We will now create a Vertex class, as follows:

public class Vertex {
final private String id;
final private String name;


public Vertex(String id, String name) {
this.id = id;
this.name = name;
}

@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((id == null) ? 0 : id.hashCode());
return result;
}

@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
...

Understanding the notion of heuristics

Let's look at heuristics; later on, we'll look at an example.

Heuristics are an approach to problem solving, learning, and discovery. We apply heuristics when we are not sure of what the goal should be; we can apply certain estimates, and these estimates can help us to optimize our search process. If finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution.

So, let's look at an example of using heuristics.

Suppose that we have a puzzle with eight tiles, arranged as shown in the Initial State cube, and we want to arrange them as they are in the Goal State cube:

To take 1 from its Initial State to its Goal State, we have to move 1 from the first tile to the last tile in the first row.

We also have to move at least two...

A brief introduction to the A* algorithm

We'll now look at how an A* algorithm works. In this algorithm, we'll calculate two costs. We'll be taking four pieces of input: our start state (a set of implicitly given states), a state transition operator, a goal state, and the heuristic value of each node. Based on those, we'll be calculating our actual cost, g(n) (which we also calculated in our Dijkstra's algorithm). Along with the actual cost, we'll calculate one more cost: the final cost (f(n)). The final cost will be the actual cost plus the heuristic cost (h(n)). The formula is as follows:

In the preceding formula, the following applies:

  • g(n) is the actual cost of traversing from the initial state to state n
  • h(n) is the estimated cost of reaching the goal from state n

We are given the following information:

[S,s,O,G,h]

In the preceding...

Implementing an A* algorithm

We will now look at how to implement an A* algorithm. Let's start with the code. We will use the same code that we used in the Dijkstra's search algorithm. The Vertex.java file is as follows:

public class Vertex {
final private String id;
final private String name;


public Vertex(String id, String name) {
this.id = id;
this.name = name;
}
// public String getId() {
// return id;
// }
//
// public String getName() {
// return name;
// }

@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((id == null) ? 0 : id.hashCode());
return result;
}

@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
...

Summary

In this chapter, you learned about heuristics, and you also learned how uniform cost and A* algorithms work.

In the next chapter, you'll learn how game playing works (in other words, how AI games work). We'll cover the rule-based system and how it works in Java.

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Key benefits

  • Leverage the power of Java libraries to build smart applications
  • Build and train deep learning models for implementing artificial intelligence
  • Learn various algorithms to automate complex tasks

Description

Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains.

Who is this book for?

Hands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.

What you will learn

  • • Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others
  • • Build machine learning models using supervised and unsupervised machine learning techniques
  • • Implement different deep learning algorithms in Deeplearning4j and build applications based on them
  • • Study the basics of heuristic searching and genetic programming
  • • Differentiate between syntactic and semantic similarity among texts
  • • Perform sentiment analysis for effective decision making with LingPipe
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2018
Length: 144 pages
Edition : 1st
Language : English
ISBN-13 : 9781789537550
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Publication date : Aug 31, 2018
Length: 144 pages
Edition : 1st
Language : English
ISBN-13 : 9781789537550
Vendor :
Oracle
Category :
Languages :

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Table of Contents

8 Chapters
Introduction to Artificial Intelligence and Java Chevron down icon Chevron up icon
Exploring Search Algorithms Chevron down icon Chevron up icon
AI Games and the Rule-Based System Chevron down icon Chevron up icon
Interfacing with Weka Chevron down icon Chevron up icon
Handling Attributes Chevron down icon Chevron up icon
Supervised Learning Chevron down icon Chevron up icon
Semi-Supervised and Unsupervised Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.3
(3 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 33.3%
1 star 66.7%
Sand George Ionut Nov 05, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
i don't recomand this book
Amazon Verified review Amazon
Florian Dec 14, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Das Buch schafft weder einen anstädnigen Überblick über AI, noch werden die angegeben Themen verständlich behandelt.
Amazon Verified review Amazon
Simon Burfield Sep 27, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This is the first book I have ever returned. I brought it to learn about deep learning which is mentioned on the front cover. Sadly, that's the only place its mentioned and it does not cover anything about deep learning. For a basic overview of AI, this should be fine.I assume the guy just never finished the book
Amazon Verified review Amazon
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