foundations of computational agents
The following are the main points you should have learned from this chapter:
Relational representations are used when an agent requires models to be given or learned before it which individuals it will encounter.
Many of the representations in earlier chapters can be made relational.
The situation calculus represents time in terms of the action of an agent, using the $init$ constant and the $do$ function.
Event calculus allows for continuous and discrete time and axiomatizes what follows from the occurrence of events.
Inductive logic programming can be used to learn relational models, even when the values of features are meaningless names.
Collaborative filtering can be used to make predictions about instances of relations from other instances by inventing hidden properties.
Plate models and the independent choice logic allow for the specification of probabilistic models before the individuals are known.