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ARTIFICIAL INTELLIGENCE FOR ROBOTICS I

CODE 104734
ACADEMIC YEAR 2021/2022
CREDITS
  • 5 cfu during the 1st year of 10635 ROBOTICS ENGINEERING (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

    Exam Board

    ARMANDO TACCHELLA (President)

    FULVIO MASTROGIOVANNI

    RENATO UGO RAFFAELE ZACCARIA (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    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

    Date Time Location Type Notes
    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