About This Chapter
Constraint Satisfaction in Artificial Intelligence - Chapter Summary
This handy chapter on constraint satisfaction in artificial intelligence was created by professional instructors to make these subjects easy to understand. Review topics including Bayes networks in machine learning, decision tree algorithms and LISP in AI at your convenience and pace. To make sure you've understood what you've learned, take the quiz offered along with each lesson or the test at the end of the chapter. Contact an expert through the Dashboard if you have any questions along the way. After you complete this chapter, you should be ready to:
- Define constraint satisfaction problems with examples
- Detail the uses of Bayes networks in machine learning
- Provide examples of neural networks in machine learning
- Outline the decision tree algorithm in data mining
- Explain the uses for Markov decision processes
- Identify the AI applications for computational logic
- Describe the importance of simultaneous localization and mapping (SLAM)
- Discuss LISP in artificial intelligence
1. Constraint Satisfaction Problems: Definition & Examples
Constraint satisfaction problems (CSPs) need solutions that satisfy all the associated constraints. Look into the definition and examples of constraint satisfaction problems and understand the process of converting problems to CSPs, using examples.
2. Bayes Networks in Machine Learning: Uses & Examples
In this lesson, we will discuss a few applications of Bayesian Software such as filtering spam e-mail, assessing medical and homeland security risks, and decoding DNA.
3. Neural Networks in Machine Learning: Uses & Examples
Neural networks play a significant role in the way companies are approaching AI and machine learning processes. This lesson will take you through the different types of neural networks and why they are so prevalent today.
4. Decision Tree Algorithm in Data Mining
Decision trees, and data mining are useful techniques these days. In this lesson, we'll take a closer look at them, their basic characteristics, and why they are so useful.
5. Markov Decision Processes: Definition & Uses
In this lesson, you will learn about the Markov Decision Process. We will discuss what its main features are and how, under specific conditions, real-life scenarios can be represented to help make the best decisions.
6. Computational Logic: Methods & AI Applications
This lesson will introduce to the basic notion of Computational Logic. Types of logic, creating logics, connecting them, and to reason with them will also be discussed.
7. Simultaneous Localization and Mapping (SLAM): Definition & Importance
In this lesson, you will be introduced to what and why of Simultaneous Localization and Mapping (SLAM). It's importance and application in Artificial Intelligence will also be discussed.
8. What is LISP in Artificial Intelligence?
One of the oldest programming languages, LISP or list processing, was developed at MIT in close proximity to researchers working on artificial intelligence (AI). Learn more about LISP and how it has helped and still helps with the development of AI.
9. Required Assignments Reminder
Sorry for the interruption to your course progress! We want to make sure you know that this course has a written assignment requirement to be completed in order to finish the course. Read on to learn where to find these assignments and how to submit them.
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