Examination arrangement

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.

Specific conditions

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.