弁別に基づく構成的帰納学習

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Learning systems find a concept based on positive and negative examples by using given terms, such as features and predicates. Most learning systems employ selective induction, and find a concept description composed of only predefined terms. However, if such terms are not appropriate, constructive induction or shift of bias are required to invent new terms. Although there has been increasing interest in systems which induce a first-order logic program from examples, there are few systems that perform constructive induction. FOCL invents new terms, i.e., new predicates, by combining existing subpredicates. Based on interaction with its user, CIGOL invents terms without any given subpredicate. This paper presents Discrimination-Based Constructive inductive learning (DBC) which invents a new predicate without any given subpredicate nor any user interaction. Triggered by failure of selective induction, DBC introduces a new predicate into a previously found incomplete clause. This is performed by searching for a minimal relevant variable set forming a new predicate that discriminates positive examples from negative ones. If necessary, DBC also recursively invents subpredicates for the definition. Experimental results show that, without interactive guidance, our system CHAMP can construct meaningful predicates on predefined ones or from scratch. Our approach is system independent and applicable to other selective learning systems such as FOIL.
社団法人人工知能学会の論文
1992-11-01

弁別に基づく構成的帰納学習

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