Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
ECML '93 Proceedings of the European Conference on Machine Learning
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Embracing causality in specifying the indirect effects of actions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Hypothesizing about causal networks with positive and negative effects by meta-level abduction
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Completing causal networks by meta-level abduction
Machine Learning
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Learning from interpretation transition
Machine Learning
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In [1] a method for inducing the effects of actions was introduced which provides a solution to the frame problem in induction. The method relied on well-known monotonic methods of ILP making it as efficient as induction of Horn Logic Programs. That proposal is not intended for the induction of the ramifications of the effects of actions (indirect effects) thus providing domain descriptions with the so-called ramification problem. In this work we introduce the induction of such ramification rules describing effects directly from other effects without mentioning the action. A framework based on causality in action formalisms is used to induce causal ramification rules. The method is shown sound and complete while efficient as the induction of action rules.