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.
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.
The main educational objectives of the course and the corresponding learning outcomes are:
Understand and comprehend the key concepts of propositional logic and first-order logic, including syntax, semantics, and resolution techniques.
Acquire a solid understanding of the theoretical foundations of various usable languages, evaluating their respective advantages.
Understand the main languages and tools for representing planning problems, such as PDDL and PDDL+.
Learn techniques for automated planning, including informed and uninformed search methods, and planning viewed as a satisfiability (SAT) problem.
Be able to model complex problems in terms of knowledge and data representation, using advanced tools and languages.
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.
Be capable of implementing automated planning algorithms to solve practical problems, including time and resource management.
Acquire practical skills in the use of languages and tools for creating AI systems, such as the implementation of search and planning algorithms.
Basic knowledge of data structures, complexity and programming.
The course will consist of frontal lessons with exercises.
Syntax and Semantics:
Simplification and Resolution Techniques:
Representations and Incomplete Procedures:
Motivations, Syntax, and Semantics:
Examples and Logical Properties:
Herbrand Theorem and Decidability:
Intended Interpretations:
Representation Languages for Planning:
Numeric Planning and Temporality:
Planning as Satisfiability (SAT):
Search and Planning:
The teaching material presented during lessons will be made available on AulaWeb.
Ricevimento: I am usually available both before and after the teaching hours and always by appointment.
ENRICO GIUNCHIGLIA (President)
LUCA ONETO
ARMANDO TACCHELLA (President Substitute)
MATTEO CARDELLINI (Substitute)
The course will be in the Second Semester:
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”.
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.
