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Ch 3: Using Artificial Intelligence in Searches

About This Chapter

Access this chapter on using artificial intelligence in searches to help you prepare for a test or get through your homework assignments. This series of lessons and quizzes can be conveniently accessed from any smartphone, tablet or computer, 24 hours a day.

Using Artificial Intelligence in Searches - Chapter Summary

This chapter on using artificial intelligence in searches includes helpful and short lessons designed to make it enjoyable and easy to learn more about concepts like game theory in artificial intelligence, machine learning and computer security risks. Work at your own pace and feel free to go back and review these lessons as many times as needed. You may find that taking the accompanying quizzes will help clarify any particularly challenging topics, or you're welcome to submit your questions through the Dashboard for our experts to answer. Once you complete this chapter, you should be ready to:

  • Understand the algorithms associated with writing pseudocode
  • List the steps for writing a program, including testing, debugging and coding
  • Define a computer security risk
  • Outline the techniques and applications of machine learning
  • Discuss machine code and high-level languages
  • Detail measurements and uncertainty in science
  • Use Bayes' theorem in AI decision-making
  • Provide the definition, uses and examples of heuristic methods in AI
  • Identify uninformed and adversarial searches in AI
  • Describe game theory in artificial intelligence
  • Discuss AI searches as a practical application of artificial intelligence

11 Lessons in Chapter 3: Using Artificial Intelligence in Searches
Test your knowledge with a 30-question chapter practice test
Writing Pseudocode: Algorithms & Examples

1. Writing Pseudocode: Algorithms & Examples

In this lesson, we will cover the writing of pseudocode by describing what it is and why we use it, and look at some common techniques. Then, we'll present a few examples to give you a better idea.

How to Write a Program: Coding, Testing & Debugging

2. How to Write a Program: Coding, Testing & Debugging

Programmers use an integrated development environment for formatting code, checking syntax, and testing programs. Learn about some of the specific tools used by programmers, such as syntax highlighting, autocompletion, and debugging.

What is a Computer Security Risk? - Definition & Types

3. What is a Computer Security Risk? - Definition & Types

Your computer is at risk! It's all over the news. We hear it every day, but what does that mean? What is a computer security risk? In this lesson, we'll define it and give some examples.

Machine Learning: Techniques & Applications

4. Machine Learning: Techniques & Applications

In this lesson, we'll take a look at machine learning, what it's all about, some techniques used in machine learning, and some applications of this cutting-edge area.

Machine Code and High-level Languages: Using Interpreters and Compilers

5. Machine Code and High-level Languages: Using Interpreters and Compilers

The only language computer hardware can understand is binary code consisting of 1s and 0s. Learn how compilers and interpreters are used to translate a computer program into binary code in this video lesson.

Measurements & Uncertainty in Science

6. Measurements & Uncertainty in Science

In this lesson, you will discover the importance of precision and accuracy in science while learning to make measurements. Also, you will understand how to perform calculations with measurements that conserve precision and limit uncertainty.

Using Bayes' Theorem in AI Decision-Making

7. Using Bayes' Theorem in AI Decision-Making

This lesson gradually develops the Bayes' theorem from its basic form to a generalized structure - used for making decisions in AI. The examples follow a step-by-step illustration of how to revise probabilities using the generalized form of Bayes' theorem.

Heuristic Methods in AI: Definition, Uses & Examples

8. Heuristic Methods in AI: Definition, Uses & Examples

Artificial intelligence allows computers to solve problems. What happens when there is no solution or finding a solution takes too long? We'll look at heuristic methods in AI and how they can be used to find approximate solutions to complex problems.

Uninformed & Adversarial Searches in AI

9. Uninformed & Adversarial Searches in AI

In this lesson we will introduce two types of search: 1) uninformed search and 2) adversarial search. For each, we will define it, outline ordering methods, and step through examples. We will also discuss the application of each to problems in human experience and in artificial intelligence.

Game Theory in Artificial Intelligence

10. Game Theory in Artificial Intelligence

In this lesson, we will understand the use of Game Theory in Artificial Intelligence. We will also discuss essential algorithms such as Minimax and few others used in this theory.

Practical Application for Artificial Intelligence: AI Searches

11. Practical Application for Artificial Intelligence: AI Searches

In this practical lesson, you will create a Java program to use an AI search. The program will find a path within a maze. You will build, compile, run, and test your program.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
Not Taken
Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
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