The following are the main points you should have learned from this chapter:

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  Relational representations are used when an agent requires models to be given or learned before it which individuals it will encounter.

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  Many of the representations in earlier chapters can be made relational.

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  The situation calculus represents time in terms of the action of an agent, using the $init$ constant and the $do$ function.

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  Event calculus allows for continuous and discrete time and axiomatizes what follows from the occurrence of events.

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  Inductive logic programming can be used to learn relational models, even when the values of features are meaningless names.

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  Collaborative filtering can be used to make predictions about instances of relations from other instances by inventing hidden properties.

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  Plate models and the independent choice logic allow for the specification of probabilistic models before the individuals are known.