Learning through overcoming temporal inconsistencies

Perpetual learning is an indispensable capability for long-lived intelligent agents (natural or artificial) to adapt to dynamic and changing environments. In our previous work on inconsistency-induced learning, i²Learning, we have proposed a general framework and several inconsistency-specific learning algorithms for perpetual learning agents that consistently and continuously improve their performance at tasks over time through overcoming inconsistencies. This paper reports the latest results of the i²Learning research on treating temporal inconsistencies as learning stimuli and defining a learning algorithm that improves an agent´s performance through refining its problem-solving knowledge as a result of overcoming temporal inconsistencies the agent encounters. We also compare our approach with related work.