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Robot Intelligence - ELE00132M

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  • Department: Electronic Engineering
  • Module co-ordinator: Dr. Mark Post
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2022-23
    • See module specification for other years: 2021-22

Module summary

This module will examine reactive, collective, and deliberative methodologies for achieving
emergent behaviour in machines and the application of these methodologies to robotic

Module will run

Occurrence Teaching cycle
A Autumn Term 2022-23

Module aims

Subject content aims:

  • Develop technical skills in the programming of algorithms for robotic autonomy
  • Develop technical skills in the use of simulation for development of autonomy algorithms
  • Develop analysis skills in evaluation of autonomy algorithms

Graduate skills aims:

  • Understand data and present it in a meaningful manner
  • Apply problem solving skills to complex problems

Module learning outcomes

After successful completion of this module, students will be able to:

Subject content learning outcomes:

  • Critically evaluate and work with a range of robotic autonomy approaches
  • Analyse the underlying principles and mathematical modeling of algorithms for autonomy
  • Apply a range of autonomy algorithms to a variety of application areas and understand their limitations in these areas
  • Critically reflect on the differences in fundamental philosophy behind various techniques developed for robotic autonomy
  • Combine autonomy approaches to solve complex problems and produce emergent behaviours


Graduate skills learning outcomes:

  • Understand abstract representations of the world and communicate them in written content
  • Apply theoretical knowledge to engineering problems

Module content

One third of the module will be devoted to swarm robotics as in ELE00114M, one third will be devoted to traditional decision-making approaches including linear regression, estimation, and classification, and one third will be devoted to probabilistic reasoning using naive Bayesian networks, inference, and Hidden Markov Models.
Six lectures and Three practicals will cover swarm robotics and its focus on algorithm simplicity and coordination, Six lectures and Three practicals will focus on numerical decision-making using regression and classification techniques, and Six lectures and Three practicals will focus on probabilistic reasoning using Bayesian techniques.


Task Length % of module mark
N/A 100

Special assessment rules



Task Length % of module mark
N/A 100

Module feedback

'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments. A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback. This can be found at The Department of Electronic Engineering aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. Students are provided with their examination results within 20 working days of the end of any given examination period. The Department will also endeavour to return all coursework feedback within 20 working days of the submission deadline. The Department would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The Department will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.

Indicative reading

  • David Barber, Bayesian Reasoning and Machine Learning.  Cambridge Univ. Press, 2017.
  • Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer-Verlag New York Inc.; Newer (Colored) edition (1 Feb. 2007).
  • Kevin P. Murphy, Machine Learning: A Probabilistic Perspective.  MIT Press (Adaptive
  • Computation and Machine Learning Series), 18 Sep 2012.
  • Russell, Shi & Eberhart - Swarm Intelligence 2001

The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.