Description

Theory
1

Theory/Practice
1

Laboratory
2

Instructors

Carlos Ramos

Contents

1- INTRODUCTION TO ARTIFICIAL INTELLIGENCE (AI)
Definition of AI; Brief historical evolution of AI; AI Areas; Intelligent Agent - sensing, communication, representation, reasoning, learning, interaction and action; Generative AI.

2- LOGIC PROGRAMMING
Predicate Logic, Unification, Automatic Proof of Theorems; Mechanisms of Reasoning, Deduction, Abduction, Induction, Modus Ponens, Modus Tollens; Logic Programming (PROLOG).

3- BASIC CONCEPTS OF AUTOMATIC PROBLEM SOLVING
Generate and Test, Hill Climbing, Depth First, Breadth First.

4- SEARCH METHODS
Best First, Branch and Bound, A *, Genetic Algorithms.

5- ARTIFICIAL NEURAL NETWORKS AND DEEP LEARNING

6- COMPUTER VISION

7- INTELLIGENT ROBOTICS

8- NATURAL LANGUAGE

9- GENERATIVE AI, LLMs, AND AGENTS

10- OPPORTUNITIES AND RISKS OF AI, ETHICAL ASPECTS
 

Learning Outcomes

CO1. Explain the concept of an intelligent agent operating in the world of the problem to be solved
CO2. Explain basic aspects of the main areas of Artificial Intelligence, including those involved in recent developments of AI
CO3. Apply the Logic Programming paradigm
CO4. Apply the concepts of Automatic Problem Solving, Search Methods, and Optimization
CO5. Create, test and evaluate a solution to a real problem of medium complexity using Search Methods and Logic Programming, working in groups.