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CM30229: Intelligent control and cognitive systems

Follow this link for further information on academic years Academic Year: 2019/0
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Honours (FHEQ level 6)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 60%, EX 40%
Further information on unit assessment Assessment Detail:
  • Weekly Quizzes (CW 10%)
  • Lab Reports (CW 50%)
  • Exam (EX 40%)
Further information on supplementary assessment Supplementary Assessment:
CM30229 - Like for Like except quizzes - instead a single short answer exam for Supp Assessment (where allowed by programme regulations)
Further information on requisites Requisites: Before taking this module you are advised to take CM20252
Further information on descriptions Description: 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 ability to evluate the commonalities and differences between natural and artificial intelligent systems.
4. To develop reserach and reading skills appropriate for short conference papers, in order to take advantage of cutting-edge research.

Learning Outcomes:
1. Students should be able to analyse and evaluate available options for mechanical real-world perception, and to consequently recommend appropriate technologies for informing robotic control.
2. Students should be able to analyse and evaluate 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 analyse and consequently 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 demonstrate sufficient research and information retrieval skills to enable them to analyse, evaluate and compare both current and classic intelligent control algorithms from journal and conference literature.

Skills:
1. Reading and assimilating technical papers.
2. Self-learning: study skills appropriate for technology professionals.
3. IT: programming skills useful for addressing contemporary commercial and scientific applications.

Content:

* 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.
* 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.
* Perception and Learning: sensor fusion, memory, and learning. The beginnings of cognition. [lab 1 due]
* Introduction to agent-based modelling; the impact of concurrency and society; simulations in policy and science; models, simplicity and explanation.
* Natural intelligence: Evolution and cognitive control, variation in cognitive strategies found in nature, individual variation in nature; perception and action selection in nature.
* 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]
* Sensing & Action primitives II: Animation and Virtual Reality. Motion capture, segment smoothing. Motion planning and basic AI for games.
* Complex planning systems, achieving multiple goals, agents with emotions and personality. Likeability, believeability and engagement.
* Ethics and philosophy of AI, can we build consciousness? What should our users believe about our agents?
Further information on programme availabilityProgramme availability:

CM30229 is Optional on the following programmes:

Department of Computer Science
  • USCM-AFB06 : BSc(Hons) Computer Science (Year 3)
  • USCM-AAB07 : BSc(Hons) Computer Science with Study year abroad (Year 4)
  • USCM-AKB07 : BSc(Hons) Computer Science with Year long work placement (Year 4)
  • USCM-AFB20 : BSc(Hons) Computer Science and Mathematics (Year 3)
  • USCM-AAB20 : BSc(Hons) Computer Science and Mathematics with Study year abroad (Year 4)
  • USCM-AKB20 : BSc(Hons) Computer Science and Mathematics with Year long work placement (Year 4)
  • USCM-AFB09 : BSc(Hons) Computer Science with Business (Year 3)
  • USCM-AAB10 : BSc(Hons) Computer Science with Business with Study year abroad (Year 4)
  • USCM-AKB10 : BSc(Hons) Computer Science with Business with Year long work placement (Year 4)
  • USCM-AFM01 : MComp(Hons) Computer Science (Year 3)
  • USCM-AAM02 : MComp(Hons) Computer Science with Study year abroad (Year 3)
  • USCM-AKM02 : MComp(Hons) Computer Science with Year long work placement (Year 3)
  • USCM-AFM14 : MComp(Hons) Computer Science and Mathematics (Year 3)
  • USCM-AAM14 : MComp(Hons) Computer Science and Mathematics with Study year abroad (Year 3)
  • USCM-AKM14 : MComp(Hons) Computer Science and Mathematics with Year long work placement (Year 3)
Department of Mathematical Sciences

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