Logics for Artificial Intelligence

You are here

Credits
6
Department
URV
Types
Elective
Requirements
This subject has not requirements
Introduction to the basic mechanisms of knowledge representation and reasoning using the formal tools of Mathematical Logic.
Web: http://moodle.urv.cat
Mail:

Teachers

Person in charge

  • Antonio Moreno Ribas ( )

Weekly hours

Theory
2
Problems
1
Laboratory
0
Guided learning
0
Autonomous learning
7

Competences

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.
  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Technical Competences of each Specialization

Academic

  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.

Transversal Competences

Solvent use of the information resources

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Objectives

  1. Understand the basic tools of Mathematical Logic and their use as a knowledge representacion and reasoning mechanism within an intelligent system.
    Related competences: CT4, CEA13, CG3,
  2. Know how to apply the tools of Mathematical Logic to solve specific problems.
    Related competences: CT6, CEA13, CEP3, CEP5, CG1,

Contents

  1. First-Order Logic
    Use of first-order logic as a mechanism for knowledge representation and reasoning.
  2. Logic Programming
    Logic programming: facts and rules. Cut operator. Negation as failure.
  3. Description logics.
    Description logics: language, reasoning mechanisms.
  4. Inheritance networks.
    Defeasible reasoning on inheritance networks.
  5. Default reasoning.
    Closed world reasoning. Circumscription. Default logic. Autoepistemic logic.

Activities

Lectures

Lectures that cover the theoretical content of the course.
Theory
30
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0
  • Theory: Lectures
Objectives: 1
Contents:

Problem sessions

Discussion of exercises on the topics covered in the course
Theory
0
Problems
15
Laboratory
0
Guided learning
0
Autonomous learning
0
  • Problems: Problem sessions
Objectives: 2
Contents:

Teaching methodology

Teaching methodologies:
* Lectures.
* Sessions with student participation.
* Autonomous work.
* Tutoring sessions.
* Preparation of evaluation tests.

Evaluation methodology

Final exam: 50%.
Individual problems solved in class: 25%.
Individual exercises: 25%.

Bibliografy

Basic:

Complementary:

Web links

Previous capacities

It is interesting, although not mandatory, to have taken an introductory course on Logic.