Comparison of tightly and loosely coupled decision paradigms in multiagent expedition

Frameworks for cooperative multiagent decision making may be divided into those where each agent is assigned a single variable (SVFs) and those where each agent carries an internal model, which can be further divided into loosely coupled frameworks (LCFs) and tightly coupled frameworks (TCFs). In TCFs, agent communication interfaces render their subuniverses conditionally independent. In LCFs, either agents do not communicate or their messages are semantically less restricted. SVFs do not address the privacy issue well. LCF agents cannot draw from collective knowledge as well as TCF agents can. However, disproportional effort has been dedicated to SVFs and LCFs, which can be attributed partially to unawareness of the computational advantages of TCFs over performance, efficiency and privacy. This work aims to provide empirical evidence of such advantages by comparing recursive modeling method from LCFs and collaborative design network from TCFs, both of which are decisiontheoretic and the latter of which is based on graphical models. We apply both to a testbed, multiagent expedition, resolve technical issues encountered, and report our experimental evaluation.