Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
Personalized Mail Agent Using Inductive Logic Programming
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Parallel Induction Algorithms for Large Samples
DS '98 Proceedings of the First International Conference on Discovery Science
A New Design and Implementation of Progol by Bottom-Up Computation
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Concurrent Execution of Optimal Hypothesis Search for Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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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.