Introduction to artificial intelligence

1. Introduction.

What is artificial intelligence?

The history of AI

Agents

Knowledge-based systems

2. Propositional Logic.

Syntax

Semantics

Proof systems

Resolution

Horn clauses

Computability and complexity

Applications and limitations

3. First-order Predicate Logic.

Syntax

Semantics

Quantifiers and normal forms

Proof calculi

Resolution

Automated Theorem Provers

Mathematical examples

Applications

4. Limitations of Logic.

The search space problem

Decidability and incompleteness

The flying penguin

Modeling uncertainty

5. Logic Programming with PROLOG.

PROLOG systems and implementations

Simple exercises

Execution control and procedural elements

Lists

Self-modifying programs

A planning example

Constraint logic programming

6. Search, Games and Problem Solving.

Introduction

Uninformed search

Heuristic search

Games with opponents

Heuristic evaluation functions

State of the art

7. Reasoning with Uncertainty.

Computing with probabilities

The principle of maximum entropy

LEXMED, and expert system for diagnosing appendicitis

Reasoning with Bayesian networks

8. Machine Learning and Data Mining.

Data analysis

The perceptron, a linear classifier

The nearest neighbor method

Decision tree learning

Learning of Bayesian networks

The naive Bayes classifier

Clustering

Data mining in practice

9. Neural Networks.

From biology to simulation

Hopfield networks

Neural Associative memory

Linear networks with minimal errors

The backpropagation algorithm

Support vector machines

Applications

10 Reinforcement Learning.

Introduction

The task

Uninformed combinatorial search

Value iteration and dynamic programming

A learning walking robot and its simulation

Q-learning

Exploration and exploitation

Approximation, generalization and convergence

Applications

Curse of dimensionality

11. Solutions for the Exercises.

Introduction

Propositional logic

First-order predicate logic

Limitations of logic

PROLOG

Search, games and problem solving

Reasoning with uncertainty

Machine learning and data mining

Neural networks

Reinforcement learning.