Failure-Driven Reminding for Incremental Learning

Abstract

Much of adult learning is gradual, almost imperceptible. Our model for this knowledge-based, incremental learning is to augment normal story comprehension processing with a failure tracking mechanism. When a comprehension rule fails, the failure and its correction are stored in an exception episode attached to the failing rule. The rule is otherwise unchanged. Subsequent failures of that rule trigger the retrieval of these exception episodes (failure-driven reminding). Rule modification occurs when classes can be found for the known exceptions. The ALFRED program is a preliminary implementation that classifies and remembers failures of everyday knowledge in the domain of political economics.