Automated Bias Shift in a Constrained Space for Logic Program Synthesis.

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Abstract

We propose a new approach to first order inductive learning using techniques borrowed from the state of the art constructive inductive ILP systems. In this respect a learning system ALPS is presented which performs a top-down iterative broadening search through the hypothesis space. ALPS uses argument selection heuristic of constructive inductive ILP systems which enables it to avoid a huge search space. It employs an automated bias adjustment procedure through a sequence of hypothesis subspaces arranged in a hierarchical lattice. Some experiments show that in benchmark logic program synthesis tasks, ALPS visits much less search space than well-known existing algorithms which perform a hill-climbing search through the hypothesis space. ALPS is also shown to be more successful in learning situations where there exists many irrelevant background predicates and where the training set comes from an unbiased source.

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