Computing
MA410: Artificial Intelligence (5 ECTS)
The course covers topics in the modern Artificial Intelligence, including: optimized tree searching, game theory, propositional and predicate logic, reasoning under uncertainty, utility, and the Prolog language.
Taught in Semester(s) 1, 2. Examined in Semester(s) 2.
Workload: 102 hours (24 Lecture hours, 18 Lab hours, 60 Self study hours).
Module Learning Outcomes.
On successful completion of this module the learner should be able to:
- Prove computational complexity of the main tree search algorithms.
- Traverse game trees to calculate optimal playing strategies.
- Manipulate compound logical statements using conjunctive normal form.
- Use inference to prove validity of logical statements.
- Use Bayesian networks to answer queries in probabilistic reasoning.
- Apply decision networks to determine utilities for a range of potential decisions.
- Complete all computer laboratory sessions satisfactorily.
Indicative Content
The course covers topics in the modern Artificial Intelligence, including: optimized tree searching, game theory, propositional and predicate logic, reasoning under uncertainty, utility, and the Prolog language.
Module Resources
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice-Hall
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