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.
Person in charge
Aida Valls (
Generic Technical Competences
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
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.
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.
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.
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..
Know the fundamental basis of Approximate Reasoning and Planning methods
Support the implementation with the use of programming languages user manuals.
Identify the possibilities and limitations of Artificial Intelligence
Apply the model of search space to decompose a problem.
Be able to discuss the results obtained on the basis of the theoretical models studied.
Formalize a problem in terms of fuzzy logic and apply reasoning methods on this uncertainty model.
1.1 Probabilistic models
1.2 Fuzzy Logic and Fuzzy expert systems
1.3 Models based on the Theory of Evidence
Exam with questions and exercises.
Exam focused mainly on Approximate Reasoning.
15 (Outside class hours) Type:
Exercise about design and development of a fuzzy expert system, using specific software tools.