  Activation-based conditional inference applies conditional reasoning to
ACT-R, a cognitive architecture developed to formalize human reasoning. The
idea of activation-based conditional inference is to determine a reasonable
subset of a conditional belief base in order to draw inductive inferences in
time. Central to activation-based conditional inference is the activation
function which assigns to the conditionals in the belief base a degree of
activation mainly based on the conditional's relevance for the current query
and its usage history. Therewith, our approach integrates several aspects of
human reasoning into expert systems such as focusing, forgetting, and
remembering.
