Discovery Science

Volume 1721 of the series Lecture Notes in Computer Science pp 277-286


Parallel Execution for Speeding Up Inductive Logic Programming Systems

  • Hayato OhwadaAffiliated withFaculty of Sci. and Tech., Science University of Tokyo Noda
  • , Fumio MizoguchiAffiliated withFaculty of Sci. and Tech., Science University of Tokyo Noda

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This paper describes a parallel algorithm and its implementation for a hypothesis space search in Inductive Logic Programming (ILP). A typical ILP system, Progol, regards induction as a search problem for finding a hypothesis, and an efficient search algorithm is used to find the optimal hypothesis. In this paper, we formalize the ILP task as a generalized branch-and-bound search and propose three methods of parallel executions for the optimal search. These methods are implemented in KL1, a parallel logic programming language, and are analyzed for execution speed and load balancing. An experiment on a benchmark test set was conducted using a shared memory parallel machine to evaluate the performance of the hypothesis search according to the number of processors. The result demonstrates that the statistics obtained coincide with the expected degree of parallelism.