public:t-713-mers:mers-23:learning_outcomes
This is an old revision of the document!
After taking the course, diligently attending the classes and doing the assignments, thoroughly reading, and actively participating in discussions, students should be able to:
- Describe how common forms of reasoning relate to next-generation AI systems
- List key reasons for using automated reasoning processes in AI
- Explain how reasoning relates to cumulative learning, autonomous hypothesis generation and autonomous reflection
- Describe state-of-the-art reasoning projects in industry and academia
- Build systems that reason through empirical experimentation
- Use a cutting-edge reasoning framework for implementing a system that reasons and understands
- Understand the difference between autonomic and allonomic AI methodologies
- Understand the relation between reasoning and system autonomy
- Explain how reasoning, cumulative learning, and autonomy can help machines handle novelty
/var/www/cadia.ru.is/wiki/data/attic/public/t-713-mers/mers-23/learning_outcomes.1699291040.txt.gz · Last modified: 2024/04/29 13:33 (external edit)