|Delivery Type||Delivery length / details|
|Practical||2 x 1 Hour Practicals|
|Lecture||28 x 1 Hour Lectures|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Written Assignment: 30 hours||40%|
|Semester Exam||2 Hours Written Exam||60%|
|Supplementary Assessment||Resubmission: Resubmission of failed/nonsubmitted coursework components or ones of equivalent value.||40%|
|Supplementary Exam||2 Hours Resit Exam||60%|
Write simple programs in Java to search for solutions using AI techniques discussed in the module.
Demonstrate an understanding of the learning methods discussed in the module.
Describe the importance of propositional and predicate logic in Artificial Intelligence systems.
Solve simple problems in propositional and first order predicate logic.
Explain the function and use of fuzzy logic.
This module begins with a motivational section on the use of AI in computer games programming: one of the cutting-edge applications of AI in use today. It then focuses on the foundational issue of search (finding solutions to problems and games), naturally followed by evolutionary algorithms, optimisation and swarms. A key concept of knowledge represention is introduced, followed by a variety for representional techniques from logic and fuzzy logic. The course finishes with the use of AI techniques for data mining.
What is required to get computers to produce AI? Philosophical debate and ethical issues. Applications of AI such as bioinformatics, business, robotics.
2. Knowledge Representation and Logic
Symbolic and sub-symbolic representations, logical representation for problems solving, inference, deduction, abduction, induction.
3. Fuzzy Logic
Fuzzy sets and rules. Fuzzy inference.
Depth- and breadth-first search. Using minimax as an example of simplified game playing. Greedy best-first search, A* search.
5. Optimisations with Evolutionary Algorithms and Swarms
Evolutionary algorithms, evolutionary loop, selection mechanisms, variation operators. Particle swarm optimisation.
6. Data Mining
Overview of data mining. KNN classification. K-means clustering. Hierarchical clustering.
7. AI in Research.
|Skills Type||Skills details|
|Application of Number||Many AI techniques require number application|
|Communication||Written skills needed for the written assessment.|
|Improving own Learning and Performance||Written assignment requires self-motivated study and work.|
|Information Technology||Inherent in the topic.|
|Personal Development and Career planning||Will feed into students' future career plans.|
|Problem solving||Written assignment promote and assess this.|
|Research skills||Assessing AI techniques for use in the written assignments requires reading and researching other materials.|
|Subject Specific Skills||AI techniques|
This module is at CQFW Level 5