COMP24412 Symbolic AI syllabus 2019-2020
22 in total, 2 per week
10 hours in total, 5 2-hour sessions.
Feedback methodsThe course has a number of lab exercises which are marked in the lab as usual, and feedback on these exercises is provided by written comments on the work and orally by the marker.
- Assessment written exam (2 hours)
- Lectures (24 hours)
- Practical classes & workshops (10 hours)
- Analytical skills
- Problem solving
On successful completion of this unit, a student will be able to:
- Describe the syntax and semantics of first-order logic and use it to model problems
- Apply reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems
- Explain the theoretical limitations of automated theorem provers
- Write Prolog programs to solve automated reasoning tasks and explain how they will execute
- Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge bases.
- Explain how these formalisms affect the reasoning process.
- Apply, demonstrate and program knowledge-based learning methods.
- Apply, demonstrate and program formal models for natural language processing in the context of semantic parsing and natural logic inference.
|Learn Prolog now!||Blackburn, Patrick, 1959-||1904987176||College||c2006.|
|Knowledge representation and reasoning||Brachman, Ronald J.,||9781558609327; 1558609326||Morgan Kaufmann||©2004.|
|Artificial intelligence : a modern approach||Russell, Stuart J.||1292024208; 9781292024202||Pearson||2014.|
|Artificial intelligence : a modern approach /||Russell, Stuart J.||9781292401171 (Proquest Ebook Central)||Pearson,|||
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.