Explanation-Based Learning in Theo
Explanation-Based Learning in Theo
Macros are formed by examining the explanations of previously
successful inferences. The TMAC (Theo-MACros) system achieves this
for Theo by composing a sequence of successful inference steps,
computing a generalization of that sequence, then storing this
macro. This accomplishes a type of explanation-based learning. These
explanations record dependencies among beliefs, so that when a belief
is changed, Theo can remove dependent beliefs. This provides a simple
forgetting mechanism for truth-maintenance.
Return to the top of this architecture.
Go to a discussion of this capability
for multiple architectures.
Current Location: Theo-Capabilities-Explanation-Based Learning