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public:t-713-mers:mers-23: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-23/learning_outcomes.txt · Last modified: 2024/04/29 13:33 by 127.0.0.1

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