Learning Improves Mobile Agent Efficiency Sam Joseph,

Mobile agent systems incur resource overheads as they acquire and maintain knowledge about their network environment. This paper presents a technique that, for a speci c set of cases, optimises network resource consumption through dynamic selection of agent interaction protocols. This process is made possible through learning on the part of static knowledge agents, which can then predict the likely length of a given search procedure. It is these predictions combined with information about the network environment that allow quantitative comparison between di erent search protocols.