Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
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
Parallel Induction Algorithms for Large Samples
DS '98 Proceedings of the First International Conference on Discovery Science
Parallel Execution for Speeding Up Inductive Logic Programming Systems
DS '99 Proceedings of the Second 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
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Application of Pruning Techniques for Propositional Learning to Progol
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
A customisable multiprocessor for application-optimised inductive logic programming
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
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Inductive Logic Programming (ILP) allows first-order learning and provides greater expressiveness than propositional learning. However, due to its tradeoff, the learning speed may not be reasonable for datamining settings. To overcome this problem, this paper describes a distributed implementation of an ILP engine, allowing speeding up optimal hypothesis search in inverse entailment according to the number of processors. In this implementation, load balancing is achieved by contract net communication between the processors, resulting in a dynamic allocation of the hypothesis search task. This paper describes our concurrent search algorithm, distributed implementation and experimental results for speeding up inverse entailment. An initial experiment was conducted to demonstrate the well-balanced task allocation.