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Artificial Intelligence Applications (AIA)

Credits Dept. Type Requirements
7.5 (6.0 ECTS) CS
  • Elective for DIE
IA - Prerequisite for DIE


Person in charge:  (-)

General goals

The objective of this subject is to complement and broaden what students learn in the compulsory subject Artificial Intelligence and the optional subjects Learning and Natural Language Processing. To achieve this goal, the subject will be redesigned and updated every semester. To enhance students' receptivity of the subject matter, this subject has an eminently practical approach and gives students a set of problems that they must solve and implement. In recent years, the subject has placed emphasis on autonomous agents and their use in e-business. Given the importance of the practical component in this subject, it has an important weight of the students' final assessment. At the end of this subject, students will have a broader vision of the methods used in artificial intelligence and their applications in the real world.

Specific goals


  1. Theoretical and practical knowledge of advanced themes in the fields of Artificial Intelligence (Agent technologies, advanced search, knowledge representation, AI logic, planning, etc.)
  2. Knowledge of methods for developing AI applications.
  3. Knowledge of real AI applications.


  1. Identify the requirements for using AI techniques to solve a problem.
  2. Analysis of a problem to determine which AI techniques are most appropriate for a given application.
  3. Use and application of AI tools and methodologies.


  1. Ability to solve problems through the application of scientific and engineering methods.
  2. Ability to create and use models of reality.
  3. Know-how to apply the solution cycle to common scientific and engineering problems: specification, coming with ideas and alternatives, design solution strategies, carrying out the strategy, validation, interpretation and evaluation of results. Ability to analyse the process on completion.
  4. Ability to logically argue the decisions taken, work done, or a given viewpoint. Ability to give well-reasoned opinions with a view to convincing others.
  5. Ability to take take decisions when faced with uncertainty or contradictory requirements.
  6. Ability to relate and structure information from various sources and thus integrate ideas and knowledge.


Estimated time (hours):

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

1. Artificial Intelligence perspectives
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 0 0 0 0 0 2,0
Introduction to fields in which AI can be applied.

2. Introduction to intelligent agents
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 2,0 2,0 0 2,0 9,0 0 19,0
What is an agent? Agents as basic building blocks. Agent types. agent-building architectures.

3. Ontologies
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 2,0 9,0 0 21,0
What is an ontology? Methods for constructing ontologies. Description logics. Ontological languages.

4. Logic systems for Artificial Intelligence
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 2,0 9,0 0 21,0
Reasoning for AI applications. Modal logics. Temporal logics. Reasoning under uncertainty.

5. Communication
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 2,0 9,0 0 21,0
The need for communication between agents. Speech Act Theory. Languages for establishing communication between agents.

6. Advanced search algorithms
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 2,0 9,0 0 21,0
Best search algorithms. Tabu Search, meta-heuristics. Genetic algorithms.

7. Planning
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 2,0 9,0 0 21,0
Description of planning problems. Planning algorithms: Linear planning, with partial order, hierarchy.

8. Co-ordination, negotiation
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 4,0 2,0 0 2,0 9,0 0 21,0
Need for co-ordination in multi-agent systems. Negotiation between agents.

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
30,0 26,0 14,0 0 14,0 63,0 0 147,0
Avaluation additional hours 3,0
Total work hours for student 150,0

Docent Methodolgy

The methodology consists of setting forth the theory in classes and then applying the concepts learnt in class and lab exercises.

Evaluation Methodgy

Evaluation is based on a final exam and a part exam, grading of course assignments, and a grade for lab work. The final and part exams will test the theoretical knowledge and the methodology acquired by students during the course. The grade for course assignments will be based on submissions of small problems set during the course. Lab grades will be based on students" reports and lab practical work carried out throughout the course.

At about half of the 4-moth term there will be an exemptive part exam, testing the first half of the course (exemptive only if the grade is 5 or more). The final exam will test both the first and the second part of the course. The first half is compulsory for those students who didn"t pass the part exam, and optional for the rest. The maximum of both grades (or the only one for the part exam) will stand as the grade for the first part.

The final grade will be calculated as follows:

GPar = part exam grade

GEx1 = 1st half of the final exam grade

GEx2 = 2nd half of the final exam grade

Total Exams grade = [max(Npar, NEx1) + NEx2]/2

Final grade= Total Exams grade * 0.5 + Exercises grade * 0.2 + lab grade * 0.3

Basic Bibliography

  • Stuart J. Russell and Peter Norvig Artificial intelligence : a modern approach- third edition, Prentice Hall, 2011.
  • Zbigniew Michalewicz, David B. Fogel How to solve it : modern heuristics, Springer,, 2004.
  • Asunción Gómez-Pérez, Mariano Fernández-López, and Oscar Corcho Ontological engineering : with examples from the areas of knowledge management, e-commerce and the semantic Web, Springer-Verlag, 2004.
  • Ghallab Malik, Dana Nau, Paolo Traverso Automated planning : theory and practice, Elsevier/Morgan Kaufmann, 2004.
  • Weiming Shen, Douglas H. Norrie and Jean-Paul A. Barthès Multi-agent systems for concurrent intelligent design and manufacturing, Taylor & Francis, 2001.

Complementary Bibliography

  • George Dyson Darwin among the machines, Penguin Books, 1999.
  • Mark D'Inverno, Michael Luck Understanding agent systems, Springer,, 2004.
  • M. Luck, R. Ashri and M. d'Inverno Agent-Based Software Development, Artech House, 2004.
  • Priest, Graham An Introduction to non-classical logic, Cambridge University Press , 2001.
  • Michael Wooldridge An Introduction to multiagent systems, John Wiley & Sons, 2002.

Web links




Previous capacities

Students must have taken the Artificial Intelligence course.


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