This course examines the paradigm of constraint programming as a tool for solving combinatorial optimization problems.
Teachers
Person in charge
Francisco Javier Larrosa Bondia (
)
Weekly hours
Theory
1
Problems
1
Laboratory
1
Guided learning
0.116
Autonomous learning
5
Competences
Generic Technical Competences
Generic
CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.
Technical Competences of each Specialization
Academic
CEA1 - Capability to understand the basic principles of the Multiagent Systems operation main techniques , and to know how to use them in the environment of an intelligent service or system.
CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
Transversal Competences
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
Ability to model optimally a discrete optimization problem and solve it using the proper tools.
Related competences:
CT6,
CEA1,
CEA13,
CG1,
There will be theory classes to introduce the fundamental theoretical concepts, classes of problems to exercirtar to use, and laboratory classes where you will see the actual technology
Evaluation methodology
Along the course several programming assignments will be evaluated. They will weight between 5% and 20% of the final grade depending on their difficulty. There also will be a final exam whose weight will be around 30%