Unit description template

 

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Unit title

Intelligent Control and Cognitive Systems

Unit provider

Joanna Bryson

Teaching provider

Joanna Bryson

Aims

1.          The students will develop practical expertise of artificial intelligence to control real-time autonomous systems, including autonomous robots, scientific simulations, and virtual-reality characters.

2.          The students will develop skills in constructing the three types of intelligent system covered

3.          To provide students with an introduction to intelligence in nature, and an understanding of commonalities and differences between natural and artificial intelligent systems.

4.          To develop skills in reading short conference papers, in order to take advantage of cutting-edge research.

Learning Outcomes

1.          Students should be able to describe available options for mechanical real-world perception, and to choose appropriate technologies for informing robotic control.

2.          Students should be able to describe a number of mechanisms for sequencing actions, and to implement appropriate mechanisms of action selection on a variety of platforms.

3.          Students should be able to form predictions of the consequences of simple actions being performed by a large number of agents.

4.          Students should be able to test predictions of emergent group behaviour through social simulation.

5.          Students should be able to describe the state of the art in acquiring and generating primitive actions for virtual reality, and to choose appropriate technologies for particular animation tasks.

6.          Students should be able to discover both current and classic intelligent control algorithms from journal and conference literature.

Skills

            Reading and assimilating technical papers.

            Self-learning: study skills appropriate for technology professionals.

            IT: programming skills useful for addressing contemporary commercial and scientific applications.

Content

1.          week: Introduction: why intelligent control is (computationally) hard, outline / review of historic strategies (proof / search based, reactive / dynamic planning, machine learning, hybrids of these). Course structure, introduction to labs. Sensing: sonar, IR, laser range finding, vision, touch. strengths, weaknesses, and approaches to use each.

2.          week: Action: mechanisms for sequencing, goal arbitration, problem spaces and contexts. Where do action primitives come from, how does morphology do work for you. Redundancy & degrees of freedom.

3.          week: Perception and Learning: sensor fusion, memory, and learning. The beginnings of cognition. [lab 1 due]

4.          week. Introduction to agent-based modelling; the impact of concurrency and society; simulations in policy and science; models, simplicity and explanation.

5.          week. Natural intelligence: Evolution and cognitive control, variation in cognitive strategies found in nature, individual variation in nature; perception and action selection in nature.

6.          week. Writing for science and engineering: special concerns for conferences, The use & nature of evidence. experiment, proof or argument? Picking conferences, knowing a literature. [lab 2 due]

7.          week. Sensing & Action primitives II: Animation and Virtual Reality. Motion capture, segment smoothing. Motion planning and basic AI for games.

8.          week. Complex planning systems, achieving multiple goals, agents with emotions and personality. Likeability, believeability and engagement.

9.          week. Ethics and philosophy of AI, can we build consciousness? What should our users believe about our agents? [lab 3 due]

10.       Guest speakers and / or PG student projects (depending on number of projects).

11.       Brief presentations by PG students doing projects.

12.       Revision week: no lectures.

Credits

6

Level

Masters / Final Year Undergraduate

Total study hours

100

JACS code(s)

 

HESA Cost Centre(s)

 

Contact person

Joanna Bryson

 

 

Availability of unit:

Period in which the unit will run

Semester II

Location of study

GTA, laboratories will require the teaching lab.

Availability

 

Will the unit be available to…

…Final Year Undergraduates?

yes

…Visiting students?

yes

 

 

Relationship to other units (irrespective of programme of study):

Pre-requisites

programming II or equivalent

Co-requisites

 

Post-requisites

 

Forbidden combinations

 

 

 

Assessment (indicate lengths and weightings):

Assessed coursework

First laboratory report, 20% [A1, 2; LO 1, 2]

Second laboratory report, 20% [A1-4; LO 2-4, 6]

Third laboratory report, 20% [A1, 2, 4, 5; LO 2, 5, 6]

Practical classes

 

Written examinations

2-hour exam 40% [A 1, 3; LO 1-5]

Oral examinations

 

Other (please specify)

 

 

 

Supplementary Assessment (tick the relevant assessment and give further details as indicated):

Like-for-like reassessment

 

Written examination only

 

Coursework only

[

Mandatory extra work

X

Other (please specify

 

Not applicable

 

 

 

Timetabling Information (ONLY TO BE COMPLETED FOR NEW UNITS):

 

Please indicate hours per session, sessions per week & semester week numbers

Staff member who will teach

Size of group

a) Lectures

2 one-hour lectures for 10 weeks,

JJB

 

b) Seminars/Tutorials

student talks one week, 2-4 hours depending on number.

 

 

 

 

 

 

 

 

 

 

c) Practical classes (labs, computers, language, etc.)

one 2-hour laboratory, 30 minute intro plus work on project for 9 weeks

2 teaching assistants

 

 

 

 

 

 

 

 

 

d) Workshop

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

e) Field courses

 

 

 

f) Other (please specify)

 

 

 

Private study time (estimate of time and indication of how it might be used)

60 hours. Undergraduates: 15 per coursework outside of lab, 15 for exam revision.

Any special facilities required:

Robot lab kits would be best, can use simulation while seeking funding for these (LEGO mindstorms or similar, need about £2,400 for 20 kits.)

Shared teaching