Planning and Approximate Reasoning

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Credits
5
Department
URV
Types
Compulsory
Requirements
This subject has not requirements
Introduction to the planning techniques as problem solving tools. The main approaches to automatic planning will be presented. The student must be able to implement a planner and solve a case study.
The second part is devoted to introduce the main concepts on approximate reasoning, focused on Fuzzy Logic. The use of fuzzy logic in rule-based systems will be presented. The student must be able to apply this methodology to a particular problem.
Web: moodle URV
Mail:

Teachers

Person in charge

  • Aida Valls ( )

Weekly hours

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

Competences

Technical Competences of each Specialization

Academic

  • CEA2 - Capability to understand the basic operation principles of Planning and Approximate Reasoning main techniques, and to know how to use in the environment of an intelligent system or service.

Professional

  • CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Generic Technical Competences

Generic

  • 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.

Transversal Competences

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

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. Know the fundamental basis of Artificial Intelligence
    Related competences: CG3,
  2. Support the implementation with the use of programming languages user manuals.
    Related competences: CEP1,
  3. Identify the possibilities and limitations of Artificial Intelligence
    Related competences: CT6, CEA2, CEP8,
  4. Apply the model of search space to decompose a problem.
    Related competences: CT3, CEA2,
  5. Be able to discuss the results obtained on the basis of the theoretical models studied.
    Related competences: CEA2, CEP1,
  6. Formalize a problem in terms of fuzzy logic and apply reasoning methods on this uncertainty model.
    Related competences: CEA2, CEP1,

Contents

  1. Approximate reasoning
    1.1 Probabilistic models
    1.2 Fuzzy Logic and Fuzzy expert systems
    1.3 Models based on the Theory of Evidence
  2. Planning techniques

Activities

Lectures and lab practise about Approximate Reasoning

Weakly, 2 hours theoretical lecture and1 h practise in laboratories.
Theory
14
Problems
0
Laboratory
7
Guided learning
0
Autonomous learning
21

Lectures and exercises about Planning.

Weakly, 2 hours theoretical lecture and1 h practise in laboratories.
Theory
14
Problems
0
Laboratory
8
Guided learning
0
Autonomous learning
21

Teaching methodology

Oral exposition fo the teacher
Practical exercises with software tools.

Evaluation methodology

Exams 50%
Practical exercises 50%

Web links

Previous capacities

Some experience in programming is recommended.