Macro-Operators Revisited in Inductive Logic Programming

For the last ten years a lot of work has been devoted to propositionalization techniques in relational learning. These techniques change the representation of relational problems to attribute-value problems in order to use well-known learning algorithms to solve them. Propositionalization approaches have been successively applied to various problems but are still considered as ad hoc techniques. In this paper, we study these techniques in the larger context of macro-operators as techniques to improve the heuristic search. The macro-operator paradigm enables us to propose a unified view of propositionalization and to discuss its current limitations. We show that a whole new class of approaches can be developed in relational learning which extends the idea of changes of representation to more suited learning languages. As a first step, we propose different languages that provide a better compromise than current propositionalization techniques between the cost of building macro-operators and the cost of learning. It is known that ILP problems can be reformulated either into attribute-value or multi-instance problems. With the macro-operator approach, we see that we can target a new representation language we name multi-table. This new language is more expressive than attribute-value but is simpler than multi-instance. Moreover, it is PAC-learnable under weak constraints. Finally, we suggest that relational learning can benefit from both the problem solving and the attribute-value learning community by focusing on the design of effective macro-operator approaches.