91248 - Fundamentals of Artificial Intelligence and Knowledge Representation

Learning outcomes

At the end of the course, the student knows the main knowledge representation techniques and reasoning methods that underlie artificial intelligence problem solving. The student is able to develop simple solvers for artificial intelligence systems.

Course contents

The course introduces the fundamental principles and methods used in Artificial Intelligence to solve problems, with a special focus on the search in the state space, planning, knowledge representation and reasoning, and on the methods for dealing with uncertain knowledge. The course will include hands-on labs and seminars on selected topics.

Prerequisites: user-level knowledge of a high-level programming language, in order to successfully understand case studies and applications presented during the lessons.

Part I (Module 1)

Module 1

• Introduction to Artificial Intelligence: historical perspective, main application fields, introduction to knowledge-based systems and architectural organization.
• Problem-solving in AI: representation through the notion of state, forward e backward reasoning, solving as a search and search strategies. Games. Constraint satisfaction problems.
• Local Search methods, meta heuristics, solving through decomposition, constraint relaxation, branch-and-bound techniques
• Introduction to Planning, Linear planning, partial order planning, graph-based methods (GraphPlan), Scheduling.

Part II (Modules 2, 3):

Module 2

• Introduction to knowledge representation and reasoning
• Representing Terminological Knowledge: semantic networks, description logics, foundation of ontologies
• Representing actions, situations, and events.
  
• Rule-based systems: Prolog and extensions, meta-interpreters, DCG, planning in prolog, Prolog for temporal reasoning with the Event Calculus, LPAD.
• Forward chaining and RETE, Drools.
  

Module 3

• Uncertainty
• Probabilistic Reasoning

 

Readings/Bibliography

A comprehensive list of textbooks is available on the Web site, and it is reported also in the course slides.

Recommended textbook:

• S. J. Russel, P. Norvig, Artificial Intelligence: A modern approach, Prentice Hall, International edition.

Further readings:

• R. J. Brachman, H. J. Levesque, Knowledge Representation and Reasoning, Elsevier, 2004.
• F. Baader, D. Calvanese, D.L. McGuinness, D. Nardi, P.F. Patel-Schneider (editors), The description logic handbook: Theory, implementation, and applications, Cambridge University Press New York, NY, USA, 2007

Teaching methods

Frontal lessons based on slides, with discussion of practical examples and lab activities. Seminars and invited lectures. Autonomous lab activities are encouraged. The lecturers will suggest possible focussed projects. Topics proposed by the students are also welcome.

Assessment methods

The exam aims at assessing the student's knowledge and skills in the course topics and it consists of three independent parts:

• Part I, covering Module 1;
• Part II, covering Modules 2;
• Part III, covering Module 3.

There will be a separate written exam for each part. Each written exam will include exercises and open questions about all the topics presented in the relevant part of the course. The final grade will be the average of [the score achieved in Part I] and [the sum of the scores achieved in Parts II and III].

Teaching tools

Relevant learning material, including course slides, will be made available via virtuale.unibo.it.

The slides will include a comprehensive list of text books and manuals.

Suggestions for further readings, slides and notes about additional topics and exercises will be made available through the web site.

Office hours

See the website of Michela Milano

See the website of Federico Chesani

See the website of Paolo Torroni