| Unit name | Intelligent Agents (Teaching Unit) |
|---|---|
| Unit code | COMS30094 |
| Credit points | 0 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Seth Bullock |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
COMS10016 Imperative and Functional Programming and COMS10018 Object-Oriented Programming and Algorithms or equivalent. COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent. COMS20017 Algorithms and Data or equivalent. Programming: Python or another major programming language (Java, C). Maths: basic linear algebra, basic statistics, some calculus, some discrete maths. |
| Units you must take alongside this one (co-requisite units) |
None. |
| Units you may not take alongside this one |
None. |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Why is this unit important?
Intelligent agents are the primary technology with which Machine Learning and Artificial Intelligence systems
interact with people. This unit introduces students to the design and deployment of intelligent agents, social agents,
multi-agent systems and multi-agent learning.
The unit will first focus on the theoretical foundations of intelligent agents before introducing different models and
methods with an applied focus. Students will explore the area through a hands-on approach in labs applying various
tools and techniques to the development of a range of intelligent agents.
How does this unit fit into your programme of study
This is an optional unit that can be taken during TB1 in Year 3. This allows students to build on the knowledge that
they have developed within the first 2 years of their study in learning about the state-of-the-art in Intelligent Agents.
An overview of content
This unit introduces and defines what an intelligent agent is and exposes students to the different approaches
involved in designing and deploying them as part of artificial intelligence systems. The unit covers a range of agent
architectures, from simple reactive and deliberative agents to social agents, multi-agent systems and multi-agent
reinforcement learning. The unit’s approach is hands-on, exploring how to build and use intelligent agents while
covering the underpinning ideas and theoretical foundations.
How will students, personally, be different as a result of the unit
Students will be aware of state of the art of Intelligent Agents and understand the benefits & limitations of their use, and how they can be applied to a variety of artificial intelligence problems.
Learning Outcomes
On successful completion of this unit, ALL students (both MAJOR and MINOR) will be able to:
When the unit is taken as the MAJOR 20 credit variant, students will also be able to:
5. Implement and evaluate intelligent agent systems.
The unit includes lectures and a series of applied labs that allow students to learn the practical aspects of designing
and deploying intelligent agents. These labs include contact time with Teaching Assistants but can also be completed
asynchronously and students are encouraged to explore the wider literature. Given the cutting-edge nature of the
content of this unit, this gives the best opportunity for students to dictate their own learning with support from
experts within the field. If taken as a MAJOR option, the unit also provides weekly coursework support sessions.
Tasks which help you learn and prepare you for summative tasks (formative):
Teaching will take place until the start of the coursework period, with coursework support in weeks 9-11, and, for
students assessed by examination, consolidation and revision sessions in Weeks 12.
The unit begins with a series of lectures which covers the theoretical background to intelligent agents research with practical labs running alongside them once the basics have been covered. These practical labs have been designed to support the theoretical content of the unit in a way that prepares students for the exam and coursework assessments. Labs include opportunities for students to discuss and check their progress on the learning outcomes of the unit. For students taking the MAJOR unit, code developed during the labs will be a foundation of their final summative assessment.
Tasks which count towards your unit mark (summative)
For students taking this unit with the Topics in Computer Science (MINOR) examination unit, it will contribute 50% towards the 20cp Topics in Computer Science exam, (equivalent to 1 hour of exam time) that will be sat during the winter examination period. This closed-book exam will assess Learning Outcomes 1, 2, 3, and 4.
For students taking this unit as a MAJOR variant, there will be two elements of assessment:
When assessment does not go to plan
Students will retake relevant assessments in a like-for-like fashion.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMS30094).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
