ARTIFICIAL INTELLIGENCE LABORATORY
Academic year and teacher
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- Versione italiana
- Academic year
- 2022/2023
- Teacher
- EVELINA LAMMA
- Credits
- 3
- Didactic period
- Annualità Singola
- SSD
- ING-INF/05
Training objectives
- Being able to design and develop Artificial Intelligence (AI) applications.
The main acquired knowledge consists of:
- how to structure a project
- definition of the architecture
- selection of software components and technologies
- application debug and testing.
The basic acquired abilities (that are the capacity of applying the acquired knowledge) are:
- project requirements analysis
- identification of the models and architectures that are best suited for the application scenario
- development of an application performing inference and/or learning using AI systems and languages (e.g., Prolog, possibly extended with probabilistic reasoning or constraints, rule-based systems, ontological languages and systems, deep neural networks). Prerequisites
- Students are advised to take the exam after at least one amongst Fundamentals of artificial intelligence (prof. Lamma), Data mining and analytics (prof. Riguzzi) and Artificial Intelligence for Constraint Optimization (prof. Gavanelli).
Course programme
- The assigned project will be done individually or in a small group of 2 people maximum. The project will concern one or more among these topics:
- heuristic search
- automated reasoning
- machine learning
- constraint processing
and will concern the development of an AI application performing inference and/or learning using AI systems and languages (such as logic programming languages, possibly with probabilistic or constraint-based reasoning, rule-based systems, ontological languages and systems, automatic classification, data mining and clustering systems, deep neural networks). Didactic methods
- This activity does not include any lecture, but students can and are invited to interact with the teachers for defining the project and for support for its development
Credits are obtained through individual or cooperative work on the assigned project. Learning assessment procedures
- The aim of the exam is to verify at which level the learning objectives previously described have been achieved.
The exam consists of the presentation of the project individually or in group. Each student of a group will have to detail his/her personal contribution in the project. Each student, working individually or in a group, will have to show that he/she has understood the application architecture and the role of the main software components. Reference texts
- On Artificial Intelligence:
S. Russell e P. Norvig, "Intelligenza artificiale. Un approccio moderno", volume 1, Pearson Education-Prentice Hall, last or any previous edition.
On the Prolog Language:
L.Console, E.Lamma, P.Mello, M. Milano: "Programmazione Logica e Prolog", UTET, Second Edition, 1997.
On Machine Learning:
Fabrizio Riguzzi. Foundations of Probabilistic Logic Programming. River Publishers, 2018.
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio. Deep learning, volume 1. MIT Press, 2016.
On Constraint Programming:
ECLiPSe documentation: http://eclipseclp.org/doc/
MiniZinc documentation: http://www.minizinc.org/resources.html
Potassco documentation: http://potassco.sourceforge.net/
Texts for further reading:
Luc De Raedt, “Logical and Relational Learning”, Springer, Series: Cognitive Technologies, 2008
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, and David Poole, “Statistical Relational Artificial Intelligence: Logic, Probability, and Computation”, Morgan & Claypool, 2016
Daphne Koller, Nir Friedman, “Probabilistic graphical models: principles and techniques”, MIT Press, 2009
N. Lavrac and S. Dzeroski, “Inductive Logic Programming Techniques and Applications”, Ellis Horwood, 1994, http://www-ai.ijs.si/SasoDzeroski/ILPBook/
K. Apt, M. Wallace: Constraint Logic Programming using Eclipse. Cambridge University Press, 2007.
Additional useful references and links will be pointed out at the course site:
http://www.unife.it/ing/lm.infoauto/lab-ia/
