Logics for Artificial Intelligence

Credits
6
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
URV;CS
Web
https://campusvirtual.urv.cat
Mail
antonio.moreno@urv.cat
Introduction to the basic mechanisms of knowledge representation and reasoning using the formal tools of Mathematical Logic.

Teachers

Person in charge

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

    Information literacy

  • 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: CEA13, CG3, CT4,
    2. Know how to apply the tools of Mathematical Logic to solve specific problems.
      Related competences: CEA13, CG1, CEP3, CEP5, CT6,

    Contents

    1. First-Order Logic
      Use of first-order logic as a mechanism for knowledge representation and reasoning.
      Formalisation. Resolution. Model Theory.
    2. Logic Programming
      Logic programming: facts and rules. Backwards reasoning. Cut operator. Negation as failure.
    3. Description logics.
      Description logics. Language: concepts, rols and constants. Operators to define complex concepts. Reasoning mechanisms.
    4. Inheritance networks.
      Defeasible reasoning on inheritance networks. Positive and negative links and paths. Admissible links and paths. Credulous extensions. Types of reasoning.
    5. Default reasoning.
      Closed world reasoning. Circumscription. Default logic. Autoepistemic logic.
    6. Knowledge graphs.
      Definition of Knowledge Graphs. Representation in RDF, RDF(S). Examples: DBpedia, wikidata. Ontologies. Queries in SPARQL.

    Activities

    Activity Evaluation act


    Lectures

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

    Problem sessions

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

    Exercises

    Exercises solved in class during the semester
    Objectives: 2
    Week: 1
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final exam

    Theoretical exam
    Objectives: 1
    Week: 15
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

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

    Evaluation methodology

    Final exam: 50%.
    Individual exercises: 50%.

    Bibliography

    Basic

    Complementary

    Web links

    Previous capacities

    It is not necessary to have taken an introductory course on Logic.