Credits
3
Types
Elective
Requirements
This subject has not requirements
, but it has got previous capacities
Department
URV;CS
Web
https://campusvirtual.urv.cat/course/view.php?id=104190
In this course, some of such AI techniques will be analysed.
Teachers
Person in charge
- Albert Oller Pujol ( albert.oller@urv.cat )
Weekly hours
Theory
1.2
Problems
0
Laboratory
0.6
Guided learning
0
Autonomous learning
3.2
Objectives
-
Probabilistic techniques applied in robotics
Related competences:
Subcompetences- Bayesian filters, Extended Kalman filters, Ant colony optimization, Particle filtering
-
Search techniques are applied in robotics
Related competences:
Subcompetences- Voronoi teselation, A*, C-space
-
Decision making techniques applied in robotics
Related competences:
Subcompetences- Intelligent control, Action selection, Task assignment
Contents
-
Probabilistic techniques
Probabilistic techniques actually applied in robotics -
Search techniques
Search techniques actually applied in robotics -
Decision making techniques
Decision making techniques actually applied in robotics
Activities
Activity Evaluation act
Theory
6h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
16h
Theory
6h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
16h
Theory
6h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
16h
Teaching methodology
For each AI methodology:Week-1. Classroom slides and paper introduction (by teacher)
Week-2. Homework: paper reading
Week-3. Paper discussion in classroom
Week-4. Report writing
Week-5. Oral presentation. Next paper introduction (by teacher)
Evaluation methodology
Report of Probabilistic methods 33%Report of Search methods 33%
Report of Decision Making methods 33%
Bibliography
Complementary
-
Scientific papers will be provided
- Múltiple authors,