INTELLIGENT AUTONOMOUS SYSTEMS

Course Information
TitleΕΥΦΥΗ ΑΥΤΟΝΟΜΑ ΣΥΣΤΗΜΑΤΑ / INTELLIGENT AUTONOMOUS SYSTEMS
CodeNIS-08-01
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorDimitrios Vrakas
CommonNo
StatusActive
Course ID40002975

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 11
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪYPOCΗREŌTIKO KATA EPILOGĪ845
Class Information
Academic Year2020 – 2021
Class PeriodSpring
Instructors from Other Categories
Weekly Hours3
Class ID
600180194
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge on Artificial Intelligence, Good knowledge of procedural programming.
Learning Outcomes
Cognitive: - Offer a foundation of Intelligent autonomous systems and their techniques, studying further their functionality and hot to implement such a system in practical scenarios - Study the interaction of Intelligent autonomous systems under real-life and complex environments trying to act on users' behalf - Capture the underlying conditions per application and setting up proper frameworks of operation for applying the taught techniques - Understand various learning techniques and adoption of techniques that are inspired by nature and its evolution Skills: - Develop software in Python language that simulates the behavior of Intelligent autonomous systems, as well as interactive variants of them - Apply mechanisms of Intelligent autonomous systems in practical problems that demand data-driven solutions - Cultivate the ability of reviewing related scientific works and fulfills the requirements for a good reporting culture, as well as a scientific perspective
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Θέματα και Προκλήσεις Αυτόνομων Συστημάτων. Ιστορικά Στοιχεία. Παραδείγματα. Αρχιτεκτονικές Ρομπότ. Βιολογική Θεμελίωση. Σχεδιασμός Ενεργειών: Ιεραρχικός Σχεδιασμός, Εύρεση Μονοπατιού, Κατάστρωση Κινήσεων. Ευφυείς Πράκτορες: Επικοινωνία, Αρχιτεκτονικές, Έλεγχος Συμπεριφοράς, Αναπαράσταση, Αρχιτεκτονικές Συστημάτων. Ευφυής Διασύνδεση με Περιβάλλον: Αισθητήρες και Εξαρτήματα δράσης. Μάθηση στα Ευφυή Αυτόνομα Συστήματα: Μάθηση με Επίβλεψη, Νευρωνικά Δίκτυα, Γενετικοί Αλγόριθμοι, Ενισχυτική Μάθηση, Μέθοδοι Monte Carlo, Μάθηση Χρονικών Διαφορών, Μάθηση Χάρτη και Προσδιορισμός Θέσης σε αυτόν.
Educational Material Types
  • Notes
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Description
Slides in electronic format, software tools & videos.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures52✓
Laboratory Work40✓✓
Reading Assigment40✓
Project
Total132
Student Assessment
Description
Written Exams weighting 100%. Bonus grades from projects. The evaluation criteria are mentioned in the course webpages.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
  • Written Exam with Extended Answer Questions (Summative)
  • Written Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Βασικές Αρχές Ρομποτικής, Maja Mataric, Κλειδάριθμος 2010 , ISBN: 960-461-354-5
Additional bibliography for study
Συμπληρωματικές σημειώσεις (διαφάνειες μαθήματος)
Last Update
02-04-2021