LEARNING AIMS

• You need to understand the basics of knowledge representations and reasoning
• You need to understand the syntax and semantics of propositional and predicate logic (including Horn clause logic)
• Be able to understand and apply resolution (binary and SLD) in predicate logic
• Understand the basics of Logic programming and Prolog
• Understand how to represent knowledge in description logics and frames and understand their relationship to predicate logic
• Be able to make simple inferences in description logic and frames
• Understand consistency-based diagnosis and being able to solve a diagnostic problem using consistency-based diagnosis
• Being able to apply the hitting-set algorithm including the optimisations
• Understand the ideas behind abductive diagnosis, and be able to solve abductive diagnostic problems
• Be able to describe the differences and similarities between consistency-based and abductive diagnosis
• Be able to apply basic probability and utility theory
• Be able to compute a probability distribution from a Bayesian network (possible by conditioning on evidence)
• Understand the certainty calculus, how to compute certainty factors and be the probabilistic interpretation of certainty factors
• Understand basics of probabilistic logics as used in AILog
• Have basic understanding of planning and dealing with actions (most of the latter is covered in the 2nd assignment)