Content

• Introduction on the role of Knowledge Representation  in AI
  (Informal introduction to different types of knowledge and propositional attitudes, possible world analysis of knowledge, the role of knowledge in problem solving, the controversies and trade-offs of KR in AI)
•  Knowledge representation in classical logic
  (Syntax, informal and formal semantics of classical logic, KR methology in classical logic)
• Extending classical logic with definitions
  (introduction to different types of definitions and inductive definitions, syntax and formal semantics of ID-logic)
• Using classical logic for problem solving
  (The role of different forms of logical inference in AI and for problem solving)
•  Epistemic modal logic
  (Reasoning about knowledge, beliefs and intentions of other agents, syntax, formal and informal semantics of modal logic, correspondence theory, application to multi-agents systems)
• Knowledge representation in probabilistic logics
  (Introduction to probabilistic logics. Case study of CP-logic: syntax, informal and formal semantics.)
• Introduction to non-logical  KR-formalisms
  (Production rules and frame-based systems)

Course material

• Slides on Toledo
• Book "Knowledge Representation and Reasoning" Ronald Brachman and Hector Levesque

Content

• Introduction on the role of Knowledge Representation  in AI
  (Informal introduction to different types of knowledge and propositional attitudes, possible world analysis of knowledge, the role of knowledge in problem solving, the controversies and trade-offs of KR in AI)
•  Knowledge representation in classical logic
  (Syntax, informal and formal semantics of classical logic, KR methology in classical logic)
• Extending classical logic with definitions
  (introduction to different types of definitions and inductive definitions, syntax and formal semantics of ID-logic)
• Using classical logic for problem solving
  (The role of different forms of logical inference in AI and for problem solving)
•  Epistemic modal logic
  (Reasoning about knowledge, beliefs and intentions of other agents, syntax, formal and informal semantics of modal logic, correspondence theory, application to multi-agents systems)
• Knowledge representation in probabilistic logics
  (Introduction to probabilistic logics. Case study of CP-logic: syntax, informal and formal semantics.)
• Introduction to non-logical  KR-formalisms
  (Production rules and frame-based systems)

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The excercise sessions address the following topics:

• KR methodology using classical logic
• Syntax , semantics and KR of definitional knowledge
• Use of inference tools for automated problem solving using declarative specifications
• Modal and multi-modal logics, Kripke structures
• Probabilistic logics, syntax and semantics of CP-logic

Course material

• Exercises and model solutions are made available on Toledo
• Students use the state-of-the-art inference system IDP to evaluate  the correctness of their solutions for KR excercises
• The system is also used to show the role of inference for automated problem solving using declarative specifications.