Abstract. Knowledge has traditionally been considered to have a beneficial effect on the performance of problem solvers but recent studies indicate that knowledge acquisition is not necessarily a monotonically beneficial process, because additional knowledge sometimes leads to a deterioration in system performance. This paper is concerned with the pmbtem of harmful knowledge: that is, knowledge whose removal would improve a system's performance. In the first part of the paper a unifying framework, called the information filtering model, is developed to define the various alternative methods for eliminating such knowledge from a learning system where selection processes, called filters, may be inserted to remove potentially harmful knowledge. These filters are termed selective experience, selective attention, selective acquisition, selective retention, and selective utilization. The framework can be used by developers of learning systems as a guide for selecting an appropriate filter to reduce or eliminate harmful knowledge. In the second part of the paper, the framework is used to identify a suitable filter for solving a problem caused by the acquisition of harmful knowledge in a learning system called LAssY. LASSY is a system that improves the performance of a PROLOG interpreter by utilizing acquired domain specific knowledge in the form of lemmas stating previously proved results. It is shown that the particular kind of problems that arise with this system are best solved using a novel utilization filter that blocks the use of lenmaas in attempts to prove subgoals that have a high probability of failing.
Some works in machine learning have used selective disclosure to remove potentially harmful knowledge that will reduce the efficiency of solvers REF .
Information filtering: Selection mechanisms in learning systems
Machine Learning
Machine Learning
Computer Science