Description

Theory
2

Laboratory
2

Instructors

Carlos Ramos

Contents

1- Problem Solving (3 hours; 5%)
Problems and their Characterization; State Space; Heuristics; Basic Methods of Finding Solutions (Depth, Width, Best)
2- Search and Optimization Methods (15 hours; 25%)
Branch and Bound; A*; Linear Programming Models; Genetic Algorithms; Taboo Search; Particle Swarm Optimization; Ant colony; Network Optimization Models
3- Planning (12 hours; 20%)
Basic Planning Concepts; Deliberative Planning; Reactive Planning; Planning with Constraints (Spatial, Temporal and Resource) and Scheduling; Project Planning (PERT)
4 - Game Theory (3 hours; 5%)
MINIMAX; Alpha-Beta cuts; Monte Carlo Search Tree
5- Decision making (27 hours; 45%)
Decision Making Process; Decision Support Systems; Modeling and Simulation; Constraints; Multi-Objective and Multi-Criterion Functions (TOPSIS and AHP); Sequential Decision Models; Decision Trees; SWOT analysis; Satisfaction Analysis; Group Decision Making and Collaborative Tools
 

Learning Outcomes

CO1- Become aware, understand and apply the concepts of Automatic Problem Solving, Search and Optimization Methods, Game Theory, Planning and Decision Making
CO2- Knowing and understanding a real problem of medium or high complexity and analyzing, investigating, designing, implementing, experimenting, reviewing, testing, synthesizing and evaluating in order to solve this problem using the methods presented in the course
CO3- Develop transversal skills related to teamwork in response to challenges (AI Challenges4Teams)