Learning First-Order Definite Theories via Object-Based Queries
We study the problem of exact learning of first-order definite theories via queries, toward the goal of allowing humans to more efficiently teach first-order concepts to computers. Prior work has shown that first order Horn theories can be learned using a polynomial number of membership and equivalence queries . However, these query types are sometimes unnatural for humans to answer and only capture a small fraction of the information that a human teacher might be able to easily communicate. In this work, we enrich the types of information that can be provided by a human teacher and study the associated learning problem from a theoretical perspective. First, we consider allowing queries that ask the teacher for the relevant objects in a training example. Second, we examine a new query type, called a pairing query, where the teacher provides mappings between objects in two different examples. We present algorithms that leverage these new query types as well as restrictions applied to equivalence queries to significantly reduce or eliminate the required number of membership queries, while preserving polynomial learnability. In addition, we give learnability results for certain cases of imperfect teachers. These results show, in theory, the potential for incorporating object-based queries into first-order learning algorithms in order to reduce human teaching effort.
KeywordsInductive Logic Programming Relevant Object Query Type Target Theory Membership Query
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