Planning and Approximate Reasoning

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Credits
5
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS;URV
Web
http://campusvirtual.urv.cat
Mail
Introduction to the planning techniques as problem solving tools. The main approaches to automatic planning will be presented. The student must be able to use different types of planners 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.

Teachers

Person in charge

  • Aïda Valls Mateu ( )

Weekly hours

Theory
2
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
5.333

Competences

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.

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.

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 Approximate Reasoning and Planning methods
    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: CEA2, CEP8, CT6,
  4. Apply the model of search space to decompose a problem.
    Related competences: CEA2, CT3,
  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
    2.1 PDDL language
    2.2 STRIPS
    2.3 Linear planners
    2.4 Graphplan
    2.5 MDP
    2.6 Reinforcement Learning

Activities

Activity Evaluation act


Exam with questions and exercises. Exam focused mainly on Approximate Reasoning.


Objectives: 1 3 5 6
Week: 15 (Outside class hours)
Type: theory exam
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Exercise about design and development of a fuzzy expert system, using specific software tools.


Objectives: 2 4 5 6
Week: 14 (Outside class hours)
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
20h

Practical exercise to solve a case study using a planner.


Objectives: 2 4 5
Week: 7 (Outside class hours)
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
22h

Lectures and lab practise about Approximate Reasoning

Weakly, 2 hours theoretical lecture and1 h practise in laboratories.

Theory
13h
Problems
0h
Laboratory
7h
Guided learning
0h
Autonomous learning
17h

Lectures and exercises about Planning.

Weakly, 2 hours theoretical lecture and1 h practise in laboratories.

Theory
13h
Problems
0h
Laboratory
8h
Guided learning
0h
Autonomous learning
17h

Exam with questions and exercises about Planning.


Objectives: 1 3 4 5
Week: 8 (Outside class hours)
Type: theory exam
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Teaching methodology

Oral exposition fo the teacher
Practical exercises with software tools.

Evaluation methodology

The student must do 2 exams, 30% each.
The student must solve several practical exercises, 40%

Bibliography

Basic:

Web links

Previous capacities

Some experience in programming is recommended.