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 consistencybased diagnosis and being able to solve a diagnostic problem using consistencybased diagnosis

Being able to apply the hittingset 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 consistencybased 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)