Niveau: Supérieur, Doctorat, Bac+8
Macro-operators Revisited in Inductive Logic Programming Erick Alphonse MIG - INRA/UR1077 78352 Jouy en Josas CEDEX FRANCE Abstract. 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 prob- lems in order to use well-known learning algorithms to solve them. Propo- sitionalization approaches have been successively applied to various prob- lems 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 cur- rent propositionalization techniques between the building cost of macro- operators and the learning cost. 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 rep- resentation language we name multi-table. This new language is more expressive than attribute-value but less expressive than multi-instance.
- relational learning
- c21 c22
- c11 c12
- space language
- language used
- ilp problem
- ilp