Tree Based Decision Strategies and Auctions in Computational Multi-Agent Systems

Abstract

This paper deals with an agent-based implementation of data mining system where a set of tasks is being processed in a distributed manner. The key role within such a system is the decision strategy of a computational agent which should consider accepting or rejecting a particular task based on various decision strategies. We present several adaptive decision strategies and compare them to traditional auction-based task distribution. Results show that optimal decision making strategy depends on the task set characteristic properties { e.g. how distinct are the best and the worst average results of each task type in dataset.