Rule induction from noisy examples

This work addresses the problem of rule learning from simple robot experiences like approaching or passing an object. An experience is a sequence of predicates computed by a perceptual system. A difficult problem encountered in this domain by rule induction algorithms is that of noise, not only in the classification of the examples, but also in the facts describing them. Due to perceptual limitations and environment complexity, the descriptions of experiences may have either missing or spurious predicates. I propose a rule induction method based on generalization of clauses under subsumption which takes into consideration the frequency of predicates across examples. Preliminary results show that this method can handle noise effectively.