CODE 104734 ACADEMIC YEAR 2021/2022 CREDITS 5 cfu anno 1 ROBOTICS ENGINEERING 10635 (LM-32) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB 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, description logics, and automated planning languages. 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 Logical Agents: knowledge-based agents, logic, propositional logic (syntax, semantics, propositional knowledge bases, inference procedures, normal forms, resolution). First-Order Logic: representation, syntax and semantics, knowledge engineering. Inference in First-Order Logic: propositional vs. first-order inference, unification and lifting, resolution. Classical Planning: definition, PDDL language, examples, planning as state-space search, planning graphs. Introduction to Reinforcement Learning: introduction, multi-armed bandits, finite markov decision processes, dynamic programming, Monte Carlo methods, Temporal-Difference learning, Planning and learning. RECOMMENDED READING/BIBLIOGRAPHY Stuart Russell, Peter Norvig - Artificial Intelligence, a Modern Approach (third edition) - Prentice Hall Richard S. Sutton, Andrew G. Barto - Reinforcement Learning, an Introduction (second edition) - MIT Press TEACHERS AND EXAM BOARD ARMANDO TACCHELLA Ricevimento: Please make an appointment with the teacher via email Exam Board ARMANDO TACCHELLA (President) FULVIO MASTROGIOVANNI RENATO UGO RAFFAELE ZACCARIA (President Substitute) 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. ASSESSMENT METHODS Ability to formalize domain of practical interest and solve them using the techniques shown in the course. Exam schedule Data appello Orario Luogo Degree type Note 10/01/2022 09:00 GENOVA Scritto 16/02/2022 09:00 GENOVA Scritto 10/06/2022 09:00 GENOVA Scritto 11/07/2022 09:00 GENOVA Scritto 13/09/2022 09:00 GENOVA Scritto