Intelligent Systems

Course Description

DESCRIPTION
In this course on Intelligent Systems, emphasis is placed on solving difficult problems, many of them NP-complete, by means of designing and using heuristics for artificial intelligence algorithms, and by developing knowledge based systems.

So, students will learn to formulate search problems and to identify and apply an appropriate solving technique. They will also be able to define and apply good heuristics to solve different problems considered difficult. Besides, they will learn to apply machine learning techniques as a way for an intelligent system to gain a certain degree of autonomy. Finally, students will learn to analyze problems whose resolution requires empirical knowledge and to design knowledge-based systems.

CONTENTS
Chapter 1. What is Artificial Intelligence?: Definitions of Artificial Intelligence. The Foundations of Artificial Intelligence. Application areas of Artificial Intelligence. Abridged history of Artificial Intelligence.
Chapter 2. Intelligent Systems: The Concept of Rationality. Problem Environment. Properties of problem environments. Problem environment and performance measure. Types of problems addressed by Intelligent Systems.
Chapter 3. Search and Heuristics: Solving problems by search techniques. Uninformed, or blind, search. Informed, or heuristic, search. How to define good heuristics and their application. Local search. On-line search. Adversarial Seach. Constraint Satisfaction Problems.
Chapter 4. Machine Learning: The definition of learning within the Artificial Intelligence context. Supervised Learning. Regression and Classification. Linear Regression. Decision Tree Learning.
Chapter 5. Knowledge Based Systems: Knowledge representation. Knowledge representation techniques. Inference and reasoning. Development of knowledge based systems that combine objects and rules. Forward chaining rule systems. Backward chaining rule systems.

METHODOLOGY
The course includes the following activities :
- Presentation and debate
- Solving exercises, problems and cases.
- Group projects applying the case solving method.
- Programming tasks
- Personal reading and study

ASSESSMENT
Group projects: 35%
Exam: 60%
Individual activities: 5%

READINGS
Russell, S. & Norving, P. Artificial Intelligence: A modern approach. 3ª Ed. Prentice-Hall. 2010.
Learning material accessible on the on-line platform for learning: Course programme and learning guide. Slides. Exercises and solutions. Problem Cases. Exams from previous years.
Programming code templates and program samples. Instructions and complementary documentation for the different learning activities and group projects. Forums to answer questions. Links to especialized web pages and recommended reading.