Learning through overcoming incompatible and anti-subsumption inconsistencies

Abstract

It is a grand challenge to build intelligent agent systems that can improve their problem-solving performance through perpetual learning. In our previous work, we have proposed a special type of perpetual learning paradigm called inconsistency-induced learning, or i2Learning, along with several inconsistency-specific learning algorithms. i2Learning is a step toward meeting the challenge. The work reported in this paper is a continuation of the ongoing research with i2Learning. We describe two more learning algorithms for incompatible inconsistency and anti-subsumption inconsistency in the context of i2Learning. The results will be incorporated into empirical studies as part of future work.