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School of Engineering and Informatics (for staff and students)

Intelligence in Animals and Machines (980G5Z)

Intelligence in Animals and Machines

Module 980G5Z

Module details for 2025/26.

15 credits

FHEQ Level 7 (Masters)

Module Outline

The module will develop an understanding of what it means for an animal or a machine to behave intelligently, and how brain and behavioural systems are adapted to enable an animal to cope effectively within its environment. We consider diverse aspects of intelligence including navigation and motor control, tool-use, language, memory and social skills. We ask how these are related to one another and how they are matched to the particular needs of animals. We finally consider what we can learn about intelligence through computational modelling by examining the challenges faced by scientists trying to create artificial systems with the same behavioural capabilities.
As well as the reading list, three papers on current research issues will be given each week to be discussed in seminars.

As well as the reading list, several papers on current research issues will be given each week to be discussed in seminars. In addition, a couple of papers which give you the flavour of the course are:

Shettleworth, S. Clever animals and killjoy explanations in comparative psychology. Trends in Cognitive Sciences, 2010
Webb, B. What does robotics offer animal behaviour? Animal Behaviour, 2000

Module learning outcomes

Demonstrate a systematic understanding of the meanings of the term 'intelligence', and an ability to critically evaluate experimental data and theoretical concepts in the field.

Synthesise research in animal cognition and the engineering of artificial intelligence, critically assess how these disciplines inform one another and evaluate the appropriateness of the methodologies used to do this.

Present a written account of specific aspects of the course subject matter based on independent reading of primary scientific and engineering literature, in the context of the wider reading of more general texts.

Develop and argue an original hypothesis that draws from the major themes of the course.

TypeTimingWeighting
Unseen ExaminationSemester 1 Assessment Week 1 Wed 13:3060.00%
Coursework40.00%
Coursework components. Weighted as shown below.
ReportPS2 Week 1 100.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

Prof Luc Berthouze

Assess convenor
/profiles/201607

Dr Nick Hay

Assess convenor
/profiles/545582

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School of Engineering and Informatics (for staff and students)

School Office:
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