course-details-portlet

IMT6171 - Real-time AI for robotics and simulated environments

About

Examination arrangement

Examination arrangement: Portfolio from project work
Grade: Passed/Failed

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio from project work 100/100 ALLE

Course content

  • Robotic control mechanisms
  • Simulation environments
  • Real time knowledge representation
  • Real time decision making and search
  • Real time scheduling and allocation
  • Decision making under uncertainty
  • Autonomous aerial, ground and underwater robots

Learning outcome

Having completed the course, the candidate should have: 

-Knowledge: The candidate is in the forefront of knowledge within the fields of artificial intelligence in robotics and simulated environments. The candidate can evaluate the expediency and application of robotic control mechanisms, aspects of simulated environments, real-time decision making and planning mechanisms, temporal representations in research and development projects. The candidate has the ability to discuss and explain robotic control mechanisms, aspects of simulated environments, real-time decision making and planning mechanisms, temporal representations methods.

-Skills: The candidate can formulate real time computational problems using robots and simulation environments. The candidate can implement real time solutions to complex problems in various robotic and simulation domains.

-General competence: The candidate has the ability to communicate and lead discussions on recent research about computational real time decision making in robotic and simulation environments. The candidate has the ability to evaluate and critique mechanisms for real time problem solving for various domains using robotics and simulations.

Learning methods and activities

-Lectures -Seminar(s)

In addition to the lectures, there will be seminar discussions.

Compulsory requirements: None

Further on evaluation

Re-sit: None

Forms of assessment: In this course, the candidates are expected to develop a solution for a real-time computing problem. The assessment is based on the portfolio of work they produce while researching and solving the given problem and a final research report on the work. The candidates must provide a presentation of results and findings in a seminar. All parts of the assessment must be passed to pass the course.

Required previous knowledge

Fundamental programming and algorithms

Course materials

Textbooks, and research articles including but not limited to: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Robin R. Murphy, 2000. An introduction to Neural Networks, Kevin Gurney, 2003. Handbook of Dynamic System Modeling, edited by Paul A. Fishwick, 2007. Engineering Applications of Artificial Intelligence, The International Journal of Intelligent Real-Time Automation, Copyright © 2012 Elsevier Ltd. Advanced issues in Artificial Intelligence and Pattern Recognition for Intelligent Surveillance System in Smart Home EnvironmentVolume 25, Issue 7, 2012.

More on the course

No

Facts

Version: 1
Credits:  5.0 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2022

Language of instruction: English

Location: Gjøvik

Subject area(s)
  • Informatics
Contact information
Course coordinator:

Department with academic responsibility
Department of Information Security and Communication Technology

Examination

Examination arrangement: Portfolio from project work

Term Status code Evaluation Weighting Examination aids Date Time Digital exam Room *
Spring ORD Portfolio from project work 100/100 ALLE
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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