OVERVIEW

The course introduces to the main themes of deductive-based Artificial Intelligence.

AIMS AND CONTENT

LEARNING OUTCOMES

The goal of the course is to provide the foundations of knowledge-based intelligent autonomous agents.

AIMS AND LEARNING OUTCOMES

The course introduces the languages and the techniques through which intelligent agents can operate autonomously on a deductive basis. The aim of the course is to provide students with the capability of formalizing domains of interest in order to treat them in the context of autonomous intelligent agents, with specific reference to propositional logics, first-order logics, and modsl/description logics.. The main result is the student's ability to frame the problems in a formal way and abstract their main features in a specification which makes computationally feasible to implement autonomous agents.

PREREQUISITES

Some preliminary knowledge of combinatorics, algebra and theoretical computer science are useful for a better understanding of the course material.

TEACHING METHODS

Lectures (possobly recorded) for the theory part;  practical sessions with the teacher to solve assigned exercises (possibly online)

SYLLABUS/CONTENT

Propositional logic: syntax, semantics, propositional knowledge bases, normal forms, automated deduction.

First-Order Logic: representation, syntax and semantics, knowledge engineering, automated deduction.

Modal/Description logics: syntax, semantics, ontology design, automated deduction

RECOMMENDED READING/BIBLIOGRAPHY

Stuart Russell, Peter Norvig - Artificial Intelligence, a Modern Approach (third edition) - Prentice Hall

TEACHERS AND EXAM BOARD

Exam Board

ARMANDO TACCHELLA (President)

ANTONY THOMAS (President)

LESSONS

LESSONS START

https://corsi.unige.it/10635/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Test with open and closed questions.

Students with certification of Specific Learning Disabilities (SLD), disabilities, or other special educational needs must contact the instructor at the beginning of the course to agree on teaching and examination methods that, while respecting the course objectives, take into account individual learning styles and provide appropriate compensatory tools. It is reminded that the request for compensatory/dispensatory measures for exams must be sent to the course instructor, the School representative, and the “Settore servizi per l'inclusione degli studenti con disabilità e con DSA” office (dsa@unige.it) at least 10 working days before the test, as per the guidelines available at the link: https://unige.it/disabilita-dsa

ASSESSMENT METHODS

Ability to formalize domain of practical interest and solve them using the techniques shown in the course.