IDENTIFYING DATA 2016_17
Subject (*) PLANNING AND APPROXIMATE REASONING Code 17685204
Study programme
Computer Security Engineering and Artificial Intelligence (2016)
Cycle 2nd
Descriptors Credits Type Year Period
3 Optional 1Q
Language
Anglès
Department Computer Engineering and Mathematics
Coordinator
MORENO RIBAS, ANTONIO
VALLS MATEU, AÏDA
E-mail
Lecturers
Web
General description and relevant information 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.

Competences
Type A Code Competences Specific
 A9 Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
Type B Code Competences Transversal
  CT1 Gestionar i comunicar informació complexa, de temes diversos, amb naturalitat, en llengua estrangera.
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A9 Representa coneixement aproximat i aplica mecanismes d'inferència sobre ell per resoldre problemes complexos.
Entén la complexitat inherent als algoritmes de planificació automàtica i l'evolució històrica de les diferents tècniques proposades en aquest camp.
Implementa mecanismes complexos de planificació automàtica i els aplica a la resolució de problemes específics.
Type B Code Learning outcomes
  CT1 Gestionar i comunicar informació complexa, de temes diversos, amb naturalitat, en llengua estrangera.
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
Type C Code Learning outcomes

Contents
Topic Sub-topic
Expert systems with approximate reasoning Probabilistic models
Evidence theory.
Fuzzy logic and reasoning based on fuzzy rules.
Introduction to planning methods Advanced techniques of planning.

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
Hours outside the classroom
(**) Total hours
Introductory activities
1 1.5 2.5
Assignments
A9
CT1
CT2
CT4
9 13.5 22.5
Material reading and studying
A9
15.8 25.7 41.5
Forums of discussion
CT2
0.2 0.3 0.5
Personal tuition
1 0 1
 
Objective multiple-choice tests
A9
2 2 4
Oral tests
A9
1 2 3
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities Presentation of the topic.
Assignments Practical session of the course, implementing the studied algorithms and methods.
Material reading and studying Presentation and explanation of the contents. Students must prepare the subject-matter
Forums of discussion Share questions with the teacher and classmates in the Forum.
Personal tuition The lecturers will have several hours during the week in which students can visit them and present them their questions or doubts concerning the content of the course. If it is considered necessary, some of the teachning hours could be devoted to personalized attention.

Personalized attention
Description
The lecturers will have several hours during the week in which students can visit them and present them their questions or doubts concerning the content of the course. If it is considered necessary, some of the teachning hours could be devoted to personalized attention.

Assessment
Methodologies Competences Description Weight        
Assignments
A9
CT1
CT2
CT4
Practical exercises on the topics of the course. 60%
Oral tests
A9
Explain some practical exercise. 10%
Objective multiple-choice tests
A9
Questions and exercises about the topics of the course. A minimum qualification is required to pass the course.
30%
Others  
 
Other comments and second exam session

Sources of information

Basic Russell, Norvig, Artificial Intelligence, a modern approach, Prentice-Hall, 2010
G. J. Klir, B. Yuan, Fuzzy sets and fuzzy logic: theroy and applications, Prentice-Hall, 1995

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(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.