CODE | 104734 |
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ACADEMIC YEAR | 2020/2021 |
CREDITS | 5 credits during the 1st year of 10635 ROBOTICS ENGINEERING (LM-32) GENOVA |
SCIENTIFIC DISCIPLINARY SECTOR | ING-INF/05 |
LANGUAGE | English |
TEACHING LOCATION | GENOVA (ROBOTICS ENGINEERING ) |
SEMESTER | 1° Semester |
TEACHING MATERIALS | AULAWEB |
The course introduces to the main themes of deductive-based Artificial Intelligence.
The goal of the course is to provide the foundations of knowledge-based intelligent autonomous agents.
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.
Some preliminary knowledge of combinatorics, algebra and theoretical computer science are useful for a better understanding of the course material.
Recorded lectures for the theory part; practical sessions with the teacher to solve assigned exercises (possibly online)
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.
Knowledge representation: ontological engineering, categories and objects, events, reasoning systems for categories (semantic networks, description logics), reasoning with default information).
Classical Planning: definition, PDDL language, examples, planning as state-space search, planning graphs.
Planning in the real world: time, schedules and resources; hierarchical planning; planning and acting in nondeterministic domains.
Introduction to Reinforcement Learning: introduction, multi-armed bandits, finite markov decision processes, dynamic programming, Monte Carlo methods, Temporal-Difference learning, Planning and learning.
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
Office hours: Please make an appointment with the teacher via email
ARMANDO TACCHELLA (President)
FULVIO MASTROGIOVANNI
RENATO UGO RAFFAELE ZACCARIA (President Substitute)
Recorded lectures for the theory part; practical sessions with the teacher to solve assigned exercises (possibly online)
September 21st, 2020
All class schedules are posted on the EasyAcademy portal.
Test with open and closed questions.
Ability to formalize domain of practical interest and solve them using the techniques shown in the course.
Date | Time | Location | Type | Notes |
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11/01/2021 | 09:00 | GENOVA | Scritto | JEMARO students must attend this exam on: January 11th, 2021 |
17/02/2021 | 09:00 | GENOVA | Scritto | JEMARO students must attend this exam on: January 11th, 2021 |
11/06/2021 | 09:00 | GENOVA | Scritto | JEMARO students must attend this exam on: January 11th, 2021 |
12/07/2021 | 09:00 | GENOVA | Scritto | JEMARO students must attend this exam on: January 11th, 2021 |
13/09/2021 | 09:00 | GENOVA | Scritto | JEMARO students must attend this exam on: January 11th, 2021 |