IDENTIFYING DATA 2015_16
Subject (*) PLANNING AND APPROXIMATE REASONING Code 17665204
Study programme
Computer Engineering: Computer Security and Intelligent Systems (2013)
Cycle 2nd
Descriptors Credits Type Year Period
6 Optional 1Q
Language
Anglès
Department Enginyeria Informàtica i Matemàtiques
Coordinator
VALLS MATEU, AÏDA
E-mail aida.valls@urv.cat
antonio.moreno@urv.cat
Lecturers
VALLS MATEU, AÏDA
MORENO RIBAS, ANTONIO
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
 T9 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
 B3 Treballar de forma autònoma amb responsabilitat i iniciativa.
Type C Code Competences Nuclear
 C1 Have an intermediate mastery of a foreign language, preferably English
 C3 Be able to manage information and knowledge

Learning outcomes
Type A Code Learning outcomes
 T9 Understand the inherent complexity of automatic planning algorithms and the historical evolution of the different techniques proposed in this field.
Implement complex mechanisms of automatic planning and apply them to solving specific problems.
Represent approximate knowledge and apply inference mechanisms to it to resolve complex problems.
Type B Code Learning outcomes
 B3 Decide how to manage and organize work and the time required to carry out a task on the basis of an initial schedule.
Present results in the appropriate way in accordance with the bibliography provided and before the deadline.
Type C Code Learning outcomes
 C1 Take notes during a class.
 C3 Critically evaluate information and its sources, and add it to their own knowledge base and system of values.

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 0 1
Practicals using information and communication technologies (ICTs) in computer rooms
B3
C1
C3
15 47 62
Lecture
T9
24 50 74
Personal tuition
1 0 1
 
Objective short-answer tests
T9
4 8 12
 
(*) 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.
Practicals using information and communication technologies (ICTs) in computer rooms Practical session of the course, implementing the studied algorithms and methods.
Lecture Presentation and explanation of the contents.
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        
Practicals using information and communication technologies (ICTs) in computer rooms
B3
C1
C3
Practical exercises on the topics of the course. 50%
Objective short-answer tests
T9
Questions and exercises on the contents of the course.
Two exams. Minimum qualification is 5 to pass the course.
50%
Others  
 
Other comments and second exam session

Sources of information

Basic Russell, Norvig, Artificial Intelligence, a modern approach, Prentice-Hall, 2010

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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.