Skip to main content
eScholarship
Open Access Publications from the University of California

Machines that Forget: Learning from retrieval failure of mis-indexed explanations

Abstract

A reasoner may fail at a cognitive task, not because it does not have appropriate knowledge with which to reason, but instead because it does not have the proper index or cue with which to retrieve such knowledge from memory. The reasoner knows this memory item; it simply cannot remember the item. This paper argues that forgetting provides an opportunity for learning through memory reorganization. A reasoner that takes full advantage of such opportunities, however, must be able to reason about its own memory system. To do so, it must possess a language for declaratively representing its reasoning failures and must reflectively inspect such representations if it is to fully explain the reason for its failure. Once such an error is understood as a memory failure, the problem of forgetting is to re-adjust the indexes so that the knowledge is properly retrieved in similar, future situations.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View