Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Social network probability mechanics
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
Learning dynamic adaptation strategies in agent-based traffic simulation experiments
MATES'11 Proceedings of the 9th German conference on Multiagent system technologies
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Change point (CP) detection is an important problem in data mining (DM) applications. We consider this problem solving in multi-agent systems (MAS) domains. Change point testing allows agents to recognize changes in the environment, to detect more accurately current state information and provide more appropriate information for decision-making. Standard statistical procedures for change point detection, based on maximum likelihood estimators, are complex and require construction of parametrical models of data. In methods of computational statistics, such as bootstrapping or resampling, complex statistical inference is replaced by a large computation volumes. However, these methods require accurate analysis of their precision. In this paper, we apply and analyze a bootstrap-based CUSUM test for change point detection, as well as propose a pairwise resampling CP test. We derive some useful properties of the tests and demonstrate their application in the decentralized decision-making of vehicle agents in city traffic.