- Understanding Logic Programming and Databases (k5)
Students can apply the principles of logic programming and logic-based databases, understanding their role in structuring and querying knowledge in AI systems.
- Implementing Knowledge Representation Formalisms (k5)
Students are able to design and implement knowledge representation structures such as semantic networks and the semantic web, using formal logic as a foundation.
- Applying Learning Methods in Logic-Based Systems (k5)
Students can implement learning algorithms, such as explanation-based learning, inductive logic programming, and relational learning, to enable automated knowledge acquisition in formal systems.
- Utilizing Case-Based Reasoning and Fuzzy Logic (k5)
Students are capable of applying case-based reasoning and fuzzy logic techniques to solve problems in knowledge representation, handling uncertain and imprecise information effectively.
- Analyzing Semantic Web and Knowledge Representation (k4)
Students can analyze the principles of the semantic web and its application in AI, understanding how formal logic and relational learning techniques enable automated knowledge discovery.
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Students acquire knowledge of foundational concepts in logic programming, databases, and knowledge representation, including techniques such as explanation-based learning, inductive logic programming, and relational learning. They learn how to apply formal logic to construct knowledge systems like semantic networks and the semantic web, gaining insights into automated learning in these systems and handling uncertain information with fuzzy logic.
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