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Artificial Intelligence (IA)

Credits Dept. Type Requirements
9.0 (7.2 ECTS) CS
  • Compulsory for DIE
  • Elective for DCSFW
  • Elective for DCSYS
ADA - Prerequisite for DIE , DCSYS
IL - Prerequisite for DIE , DCSYS , DCSFW


Person in charge:  (-)

General goals

This subject presents an array of problems that are dealt with in artificial intelligence, as well as the theoretical foundations of AI and its general applications. The subject focuses on the two basic areas of artificial intelligence: problem solving (including state space, Heuristic search and Constraint Satisfaction), and the Knowledge representation. To round out this approach, students will also be introduced to two topics that currently have a more important presence in practical applications and research: the Natural Language Processing and Knowledge-Based Systems. The subject has a practical focus.

Specific goals


  1. Scope and need for Artificial Intelligence techniques.
  2. Basic concepts regarding problem-solving and the representation of knowledge.
  3. Basic concepts underlying knowledge-based systems, design and construction of knowledge-based systems.
  4. Basic concepts in techniques for treating natural language, use of tools and analysis of applications.
  5. Basic knowledge of languages using AI applications.


  1. Analysis of a problem to determine which AI techniques are most appropriate for a given application.
  2. Ability to analyze the knowledge needed to solve a problem.
  3. Ability to extract and represent the knowledge needed to construct an application in the fields of knowledge-based and natural language processing systems.


  1. Ability to solve problems through the application of scientific and engineering methods.
  2. Ability to create and use models of reality.
  3. Ability to design and carry out experiments and analyse the results.
  4. Know-how to apply the solution cycle to common scientific and engineering problems: specification, coming up with ideas and alternatives, design solution strategies, carrying out the strategy, validation, interpretation and evaluation of results. Ability to analyse the process on completion.
  5. Ability to logically argue the decisions taken, work done, or a given viewpoint. Ability to give well-reasoned opinions and reasons with a view to convincing others.
  6. Ability to analyze and summarise issues.


Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

1. Introduction to Artificial Intelligence (AI)
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 1,0 0 0 0 0 3,0
History, reasons for, and descriptions of the various AI fields.

2. Problem-solving
T      P      L      Alt    Ext. L Stu    A. time Total 
13,0 11,0 6,0 0 28,0 10,0 0 68,0
Introduction to the methods for automatically solving problems: State-space representation, smart and local search algorithms, games and problems of satisfying restraints.

3. Knowledge representation
T      P      L      Alt    Ext. L Stu    A. time Total 
5,0 5,0 1,0 0 4,0 9,0 0 24,0
Introduction to techniques for representing knowledge. Reasons for representing knowledge. Procedural representations and production systems. Structured representations, frames and ontologies.

4. Knowledge-based systems
T      P      L      Alt    Ext. L Stu    A. time Total 
9,0 6,0 6,0 0 28,0 10,0 0 59,0
Introduction to knowledge-based systems. Need for knowledge to resolve complex problems. Relationship with representation techniques, special features. Knowledge Engineering. Learning. Approximate reasoning.

5. Natural language treatment
T      P      L      Alt    Ext. L Stu    A. time Total 
7,0 4,0 0 0 0 9,0 0 20,0
Introduction to natural language processing. Language levels. Lexical and morphological analysis. Syntactic and semantic analysis. Definite clause grammars. Applications.

6. Automatic learning
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 0 0 0 0 0 2,0
We introduce the need of learning to raise the capacities of the knowledge-based systems and to solve problems that would have a very high cost if they weren"t solved in an automated way.

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
38,0 26,0 14,0 0 60,0 38,0 0 176,0
Avaluation additional hours 4,0
Total work hours for student 180,0

Docent Methodolgy

The classes are split into theory, problem, and lab sessions. The theory sessions develop the core knowledge of the course. The classes of problems let students delve into the techniques and algorithms explained in the theory sessions in greater depth.

The lab classes involve small practical assignments using tools and languages appropriate for AI purposes. This work practices and builds on the knowledge imparted in the theory classes.

Evaluation Methodgy

Assessment is based on a part exam, a final exam, and a lab grade.

The mid-term exam will not confer any exemption and will be held in class hours. Students failing to sit or pass the part exam will only be assessed on their performance in the final exam.

The lab grade will be based on student reports on the practical work.

The final grade will be calculated as follows:

Final Grade = max (part exam grade * 0.15 + Final exam grade * 0.55, Final exam grade * 0.7) + Lab grade * 0.3

Basic Bibliography

  • Russell, Stuart; Norvig, Peter Artificial intelligence: a modern approach   (third edition), Prentice Hall, 2009.
  • Brachman, Ronald, Levesque, Hector Knowledge Representation and Reasoning, Morgan Kaufmann , 2004.
  • Ruslan Mitkov, [editors] The Oxford handbook of computational linguistics, Oxford University Press, 2003.
  • Joseph Giarratano, Gary Riley Expert systems : principles and programming, Thomson Course Technology, 2005.
  • Koller, Daphne, Friedman, Nir Probabilistic Graphical Models: Principles and Techniques, The MIT press, 2009.

Complementary Bibliography

  • Nils J. Nilsson Artificial intelligence : a new synthesis, Morgan Kaufmann Publishers, 1998.
  • Francisco Escolano ... [et al.] Inteligencia artificial : modelos, técnicas y áreas de aplicación, Thomson, 2003.
  • Luger, George F. Artificial intelligence : structures and strategies for complex problem solving, Addison Wesley Longman, 2005.
  • Avelino J. González, Douglas D. Dankel The Engineering of knowledge-based systems : theory and practice, Prentice Hall, 1993.
  • James Allen Natural language understanding, Benjamin /Cummings, 1995.
  • Rina Dechter Constraint processing, Morgan Kaufmann Publishers, 2003.
  • Jackson, P. Introduction to Expert Systems, Addison-Wesley, 1990.

Web links


Previous capacities

- Basic concepts of propositional logic and first order logic
- Ability to formulate problems in terms of logic.
- Logical inference. Resolution. Resolution strategies.
- Tree and graph structures, shortest-path and search algorithms.
- Basic principles of complexity.

Accordingly, students should have passed IL and ADA before taking the AI course.


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