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The application of cognitive mechanisms to support knowledge acquisition is,from our point of view, crucial for making the resulting models coherent,efficient, credible, easy to use and understandable. In particular, there aretwo characteristic features of intelligence that are essential for knowledgedevelopment: forgetting and consolidation. Both plays an important role inknowledge bases and learning systems to avoid possible information overflow andredundancy, and in order to preserve and strengthen important or frequentlyused rules and remove (or forget) useless ones. We present an incremental,long-life view of knowledge acquisition which tries to improve task after taskby determining what to keep, what to consolidate and what to forget, overcomingThe Stability-Plasticity dilemma. In order to do that, we rate rules byintroducing several metrics through the first adaptation, to our knowledge, ofthe Minimum Message Length (MML) principle to a coverage graph, a hierarchicalassessment structure which treats evidence and rules in a unified way. Themetrics are not only used to forget some of the worst rules, but also to set aconsolidation process to promote those selected rules to the knowledge base,which is also mirrored by a demotion system. We evaluate the framework with aseries of tasks in a chess rule learning domain.

Forgetting and consolidation for incremental and cumulative knowledge acquisition systems