OVERVIEW

The Artificial Intelligence (AI) course explores the design and implementation of intelligent systems capable of performing complex tasks that require cognitive abilities similar to those of humans. Among the key areas of AI, special emphasis is placed on the study of knowledge representation techniques and automated planning, focusing on the modeling of complex systems and on automated reasoning techniques based on the defined models.

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of the course is to introduce students to some fundamental themes of Artificial Intelligence, mainly on the "deductive" side of the discipline. Students will learn the basics of propositional logic and first-order predicate logic and will apply them in the context of knowledge representation using reasoning techniques seen in class. Additionally, in addition to the basic skills related to knowledge representation techniques and reasoning techniques, the course will address the issues and basic techniques of heuristic search and automatic planning.

AIMS AND LEARNING OUTCOMES

The main educational objectives of the course and the corresponding learning outcomes are:

Knowledge Objectives and Outcomes

1. Understand and comprehend the key concepts of propositional logic and first-order logic, including syntax, semantics, and resolution techniques.

2. Acquire a solid understanding of the theoretical foundations of various usable languages, evaluating their respective advantages.

3. Understand the main languages and tools for representing planning problems, such as PDDL and PDDL+.

4. Learn techniques for automated planning, including informed and uninformed search methods, and planning viewed as a satisfiability (SAT) problem.

5. Be able to model complex problems in terms of knowledge and data representation, using advanced tools and languages.

Practical Objectives and Outcomes

1. Be able to apply logic and knowledge representation techniques to solve real-world problems in various contexts, such as robotics, resource management, and process optimization.

2. Be capable of implementing automated planning algorithms to solve practical problems, including time and resource management.

3. Acquire practical skills in the use of languages and tools for creating AI systems, such as the implementation of search and planning algorithms.

PREREQUISITES

Basic knowledge of data structures, complexity and programming.

TEACHING METHODS

The course will consist of frontal lessons with exercises.

SYLLABUS/CONTENT

Introduction to the Course

• Introduction to the subject and the course objectives.

Propositional Logic

Syntax and Semantics:

• Study of the fundamentals of propositional logic, including normal forms (NNF), conjunctive normal form (CNF), and Tseitin's transformation.

Simplification and Resolution Techniques:

• In-depth exploration of resolution techniques, including the DPLL (Davis-Putnam-Logemann-Loveland) procedure.

Representations and Incomplete Procedures:

• Introduction to OBDD (Ordered Binary Decision Diagrams) and the limitations of propositional logic.
• Discussion and practice exercises on propositional logic.

First Order Logic (FOL)

Motivations, Syntax, and Semantics:

• Introduction to the basics of first-order logic, understanding its syntax and semantics.

Examples and Logical Properties:

• Discussion of examples of satisfiability, validity, logical equivalence, logical consequence, and normal forms such as prenex and Skolem normal forms.

Herbrand Theorem and Decidability:

• Study of the Herbrand Theorem, decidability of formulas in finite domains, and semi-decidability of first-order logic.

Intended Interpretations:

• Equality and other logical symbols.
• Discussion of practical examples and aspects beyond first-order logic.
• Practical exercises on first-order logic.

Automated Planning

Representation Languages for Planning:

• Study of languages for representing planning problems.
• Practical exercises on the use of these languages.

Numeric Planning and Temporality:

• Introduction to languages for numeric planning, such as PDDL2.1.
• Exploration of PDDL+ for handling time, processes, and events in planning problems.

Planning as Satisfiability (SAT):

• Motivations and insights into planning viewed as a satisfiability problem.
• Techniques for reducing planning problems to SAT.
• Discussion of termination conditions.

Search and Planning:

• Introduction to uninformed search techniques for solving planning problems.
• Study of informed search techniques, such as graph search and planning as a search process.

RECOMMENDED READING/BIBLIOGRAPHY

The teaching material presented during lessons will be made available on AulaWeb.

TEACHERS AND EXAM BOARD

Exam Board

ENRICO GIUNCHIGLIA (President)

LUCA ONETO

ARMANDO TACCHELLA (President Substitute)

MATTEO CARDELLINI (Substitute)

LESSONS

LESSONS START

The course will be in the Second Semester:

• Lectures: from 17 February to 30 May 2025
• Didactic suspension: 24/25/26 March 2025
• Ordinary examination session: 2 June to 19 September 2025

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The final exam will be a test with exercises on:

Propositional logic, modeling, and reasoning

First-order logic, modeling, and reasoning

Planning, modeling, and procedures

Search, uninformed and informed

Depending on the results and the content of the test taken, an oral examination may be required.

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

The exam will be assessed by evaluating the answers to the proposed exercises and to the questions in the test. In addition to correctness, the completeness of the answers will also be assessed, with particular attention to how their relevance wrt the proposed text.

Exam schedule