T. Sugawara et V. Lesser, Learning to improve coordinated actions in cooperative distributed problem-solving environments, MACH LEARN, 33(2-3), 1998, pp. 129-153
Coordination is an essential technique in cooperative, distributed multiage
nt systems. However, sophisticated coordination strategies are not always c
ost-effective in all problem-solving situations. This paper presents a lear
ning method to identify what information will improve coordination in speci
fic problem-solving situations. Learning is accomplished by recording and a
nalyzing traces of inferences after problem solving. The analysis identifie
s situations where inappropriate coordination strategies caused redundant a
ctivities, or the lack of timely execution of important activities, thus de
grading system performance. To remedy this problem, situation-specific cont
rol rules are created which acquire additional nonlocal information about a
ctivities in the agent networks and then select another plan or another sch
eduling strategy. Examples from a real distributed problem-solving applicat
ion involving diagnosis of a local area network are described.