Coursework

Acquired Intelligence and Adaptive Behaviour 2025

Deadline: May 13th, 16:00 hrs

Extension: 6-8 pages + references + appendices (which may include code)

Submission format: PDF

You have to write a technical report describing an investigation related to the topics covered in

class. For choosing the specific topic of investigation, you have to make a number of choices

related to (1) environment, (2) agents, and (3) adaptation mechanics, as explained bellow.

1. Environment:

You can choose to work with one of three possible setups:

a) Physical robotics.

b) Robot simulation, considering details of the physical environment.

c) An abstract, simplified environment.

Please note that (a), (b) and (c) are ordered according to their difficulty (with (a) being the most

difficult and (c) the easiest). Marking will consider the challenges involved with each scenario,

being more generous for more difficult setups and stricter with simpler setups

The difficulty of your environment & the problem you apply learning, evolution or other

optimisation algorithms to, will affect the maximum mark which you can receive, if you produce

an excellent report. The following table shows how your choice of environment and problem

difficulty will affect your highest possible mark. For example, if you use a physical robot we will

take the extra work that entails into account, and learning or evolving behaviours of medium

difficulty would be enough to make a distinction level mark possible, but if you only work with a

simulated robot a higher level of difficulty would be required to achieve the same kind of mark.

Environment

type

Physical

robot

Simulated

robot only

Simpler, abstract

agent/environment

Mark band

70% and above Medium High Very high

60-69% Low Medium High

40-59% Low Low Medium

2. Agent(s):

You have to choose what type of agent/agents you will study:

• You can consider a scenario with a single agent, or one with multiple interacting agents.

• You need to define the “anatomy” of the agent, including what sensors it has, what type

of actions it can do, and what is the internal structure connecting these (i.e. its brain).

• If you do evolutionary processes, you need to define the genome of the agent and its

encoding.

3. Adaptation mechanisms (algorithm):

You have to choose one or more mechanisms that will let the agent to adapt to its environment:

a. Evolution — GAs or other stochastic methods (particle filters, etc).

b. Learning — back propagation, reinforcement learning, Hebbian learning, etc.

c. A combination of methods involving both evolution and learning.

d. Other methods (speak with the TAs).

Research topic:

After these choices have been made, you have to decide what is the specific topic of your

investigation. You can investigate a question that you find most interesting, related to what we

have seen in class. If you need inspiration, below you can find some ideas:

• Compare the performance of various types of GAs for a given task.

• Train a deep neural network using different GAs and compare against back-propagation.

• Study the effect of different genotype-phenotype encodings.

• Evaluating the impact of different choices of hyperparameters in various algorithms.

• Evaluating sensor/input configuration.

• Compare performance of algorithms of evolution vs learning for the same task.

• Design an evolutionary system where learning emerges.

• Design a scenario in which learning feeds back into evolution.

• Evaluate how behaviour becomes more complex by putting multiple evolving/learning

agents to interact with each other.

• Other ideas...

Please feel free to search for your own topic. When choosing a topic, please make sure to do the

following:

1. Identify a specific hypothesis that you want to test.

2. Perform some analysis to test the hypothesis.

3. Use the results of your analysis to reach a conclusion.

Report structure:

Here is a suggested structure for the report:

1. Introduction: a general explanation of the investigation.

2. Hypothesis and methods: a description and explanation of your choices for environment,

agent, and adaptation mechanism, together with a clear presentation of your driving

hypothesis.

3. Results: a description of the experiments you did and what you found.

4. Discussion: a presentation of your conclusions related to your hypothesis. You can

mention implications of these conclusions, and also describe possible next steps for future

work that could eventually continue what you did.

Remarks:

- Code submissions must include information about which part of the code produces which

figure in their report. A failure to do this will be directly penalised by a subtraction of 10

marks

- There are an infinite number of potential investigations. Different students are expected to

develop non-overlapping topics (please don’t copy what others are doing, it is very easy

to note, and we don’t want to have to report you for academic misconduct).

- You can use AI tools (e.g. Chat-GPT) to help you with the writing style of parts of your

report. If you use them, you must acknowledge to what degree different sections used

such tools. Failure to acknowledge this (which is easy to note) will be severely penalised.

- Write as concisely as you can. If you can fit your ideas into 6 pages that is great; marks

are not related to length per se.

Marking scheme

As a guide, the contributions of different aspects of the report run as follows:

• Technical (30): The quality of your code and the algorithms you present.

• Presentation (20): The quality of writing and organization of the submitted

document, the quality of the figures and diagrams.

• Context (20): The extent to which you have motivated the work and discussed the

results in the context of the ideas presented in the lectures.

• Research (30): The extent to which you've gone beyond the lecture material and

brought in ideas from the course reading, from other sources and your own ideas.

The marking criterion will closely follow what you should expect for the 3rd year project.

• 90% – 100%: A truly outstanding project. The project outcomes (system, theory,

empirical evaluation) should be essentially faultless, well-structured and carefully

tested, proved or rigorously evaluated. There should be full achievement of

objectives and evidence of original thought. The project objectives must be very

demanding and there should be a wide range of cogently-justified project extensions.

The report should be superbly organised and presented and lucidly written. The

quality of the research and report should be equally high. The work should be of

publishable quality in a peer-reviewed national conference.

• 80% – 89%: An outstanding project. The project outcomes (system, theory, empirical

evaluation) should be essentially faultless, well-structured and carefully tested,

proved or rigorously evaluated. There should be full achievement of demanding

objectives and evidence of original thought. The report should be well organised and

presented and clearly written.

• 70% – 79%: Students will show an understanding of all aspects of the project

material, producing work without significant error or omission. Project objectives

should be reasonably demanding and fully achieved. The report should display

excellent organisational and presentational skills, and contain a thorough evaluation

and objective critical reflection.

• 60% – 69%: The project should be competent in all respects. The project's primary

objectives are somewhat demanding and should be substantially achieved to a

reasonable standard. Students will show an understanding of the technical and

professional issues involved. The presentation and organisation of the report should

be clear.

• 50% – 59%: The project should be competent in most respects. The project

objectives may not be very demanding but should be achieved to a reasonable

standard. The presentation and organisation of the report should be reasonably

clear. There may be some signs of weakness, but overall the grasp of the topic should

be sound.

• 40% – 49%: The project will indicate a basic understanding of the methods to be used

and how to organise and present the work in the report, but will not have gone beyond

this, and there may well be signs of confusion about more complex material. There

should be fair work towards the project objectives and the final report must clearly

represent a development of the interim report.

• 30% – 39%: There should be work towards the project objectives, but significant

issues are likely to be neglected. There may be significant errors or misconceptions

in the project. The final report may represent little progress with respect to the interim

report.

• 15% – 29%: The project may contain some correct and relevant material, but most

issues are neglected or are covered incorrectly. There should be some signs of

appreciation of the project requirements.

• 0% – 14%: Very little or nothing that is correct and relevant and there is no real

appreciation of the project requirements.