Inteligencia Artificial

Here you can find all the slides used in theory sessions. You can also find other interesting reference material

All the slides (for taking notes) (one per page)

Lecture notes of the course (in Spanish)

TOPIC 1: Introduction to Artificial Intelligence

TOPIC 2: Problem solving and search

You can try the implementation of the TSP problem that is in the demos of the AIMA clases , you can compare the Hill Climbing algorithm with the Simulated annealing using different parameters
This is a demos of genetic algorithms. This demo tries to generate a sentence using random strings of characters. You can play with the different parameters of the genetic algorithms.
You can use the constraint satisfaction algorithms with this program (CSPeditor). You can graphically draw the constraints graph and use the chronological bactracking and forward checking algorithms, it has also the variable ordering heuristic. You have some problems from the problem collection that you can try. The program is incomplete (alpha version) and unfortunatelly has no manual.

Other links
- Demos about the algorithms of Heuristic search
  
  Explanation and demos of different heuristic search algorithms using classic examples. You can see the execution of the algorithms and the resulting seach tree (Master thesis is spanish). All the web pages in a zip file
- Genetic algorithms and artificial life

TOPIC 3: Knowledge representation

To see how production rule systems work you can use this interpreter of pseudo-prolog (CILog) from the demos of AISpace. The program allows to see how the goals and instantiations are being generated. The zip files also contains a problem from the problem collection and a rule program to solve graph coloring problems.

Introduction to ontology developement:
"Natalya F. Noy and Deborah L. McGuinness" Ontology Development 101: A Guide to Creating Your First Ontology " Stanford University"

TOPIC 4: Knowledge Based Systems

You can test the editor of bayesian networks from AISpace. It allows to desing bayesian networks and solve queries. It uses the variable elimination algorithm and you can solve a query step by step. The version of the algorithm that uses is different from the one that is explaned in theory class, though.
The program has some bayesian networks that you can use and in this zip file you have a version of the example of CLIPS program explained in laboratory class (Auto repair diagnose).
The program can show how the probabilities od the nodes changes if we change the observed variables.
This demo shows a set of fuzzy rules that control a crane and allow to move a crate from one place to another. The rules can be edited to change the behaviour of the crane.