Bridging the lesson distribution gap

Authors:
David W. Aha;Rosina Weber;Héctor Muñoz-Avila;Leonard A. Breslow;Kalyan Moy Gupta
Affiliations:
Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC;Department of Computer Science, University of Wyoming, Laramie, WY and Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC;Department of Computer Science, University of Maryland, College Park, MD and Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC;Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC;IIT Industries, Alexandria, VA
Venue:
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Year:
2001

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Abstract

Many organizations employ lessons learned (LL) processes to collect, analyze, store, and distribute, validated experiential knowledge (lessons) of their members that, when reused, can substantially improve organizational decision processes. Unfortunately, deployed LL systems do not facilitate lesson reuse and fail to bring lessons to the attention of the users when and where they are needed and applicable (i.e., they fail to bridge the lesson distribution gap). Our approach for solving this problem, named monitored distribution, tightly integrates lesson distribution with these decision processes. We describe a case-based implementation of monitored distribution (ALDS) in a plan authoring tool suite (HICAP). We evaluate its utility in a simulated military planning domain. Our results show that monitored distribution can significantly improve plan evaluation measures for this domain.