Learning concepts from sensor data of a mobile robot
Machine Learning - Special issue on robot learning
Learning Logical Definitions from Relations
Machine Learning
Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Efficient Induction of Executable Logic Programs from Examples
ASIAN '97 Proceedings of the Third Asian Computing Science Conference on Advances in Computing Science
Tow-down Induction of Logic Programs from Incomplete Samples
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Inducing Shogi Heuristics Using Inductive Logic Programming
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Inductive Logic Programming and Embodied Agents: Possibilities and Limitations
International Journal of Agent Technologies and Systems
| Hi-index | 0.00 |
This paper investigates a method for collecting examples iteratively in order to refine the results of induction within the framework of inductive logic programming (ILP). The method repeatedly applies induction from examples collected by using previously induced results. This method is effective in a situation where we can only give an inaccurate teacher. We examined this method by applying it to robot learning, which resulted in increasing the refinement of induction. Our method resulted in the action rules of a robot being learned from a very rough classification of examples.