Adaptive & Learning Agents - COM00066M

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  • Department: Computer Science
  • Module co-ordinator: Dr. Dimitar Kazakov
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2019-20

Module will run

Occurrence Teaching cycle
A Spring Term 2019-20

Module aims

This module studies the use of Agents and Multi-agent Systems as a modelling paradigm, with a focus on adaptation and learning. The aim of the module is to advance the student's knowledge in these topics and to gain practical skills in applying a selected range of such advanced techniques in several application areas.

Module learning outcomes

  • The ability to understand and modify an existing rule-based multi-agent system;
  • the ability to define a range of agent behaviours and represent them in a form that is well suited to machine learning/selection ;
  • the ability to apply a selected range of advanced evolutionary algorithms and machine learning techniques in the context of the intelligent (learning, evolving) agent paradigm;
  • the ability to model processes in populations of agents using both hand-written and learned mathematical models.
  • The successful student will be able to combine all of the above in an integrated way and apply it to a relevant problem of interest.

Assessment

Task Length % of module mark
Essay/coursework
Adaptive & Learning Agents - Open Assessment
N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Adaptive & Learning Agents - Open Assessment
N/A 100

Module feedback

Students are required to submit a brief report from each practical as evidence of their continuous progress with the material. Feedback to all (not individual ones) may be given in the lectures, if deemed of didactic value.

Indicative reading

*** Russell, S. and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson, 3rd edition, 2013

** Alonso, E., D. Kazakov and D. Kudenko (eds.), Adaptive Agents and Multi-Agent Systems, Springer, 2003

** Goldberg, D., Genetic Algorithms, Addison-Wesley Longman Publishing, 1989

** Mitchell, T., Machine Learning, McGraw-Hill, 1997

** Roberts, S., An Introduction to Progol. Technical Manual. URL: http://www-course.cs.york.ac.uk/alas/notes/manual.pdf, University of York, 1997

* Dawkins, R., The extended phenotype, Oxford University Press, 1982

+ Baldwin, J.M., A new factor in evolution, The American Naturalist 30, 1896



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.