Automated Bias Shift in a Constrained Space for Logic Program Synthesis

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概要

• 論文の詳細を見る

• 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.

• 社団法人　人工知能学会の論文
• 2001-11-01

著者

• Numao Masayuki

  Department Of Computer Science Tokyo Institute Of Technology

• Mofizur Rahman

  Department Of Computer Science And Engineering Bangladesh University Of Engineering And Technology

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

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