public:t-713-mers:mers-24:learning_outcomes
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/pages/public/t-713-mers/mers-24/learning_outcomes.txt · Last modified: 2024/08/16 21:33 by thorisson