Using a Dynamic Neural Network to Model Team Performance for Coordination Algorithm Configuration and Reconfiguration of Large Multi-Agent Teams

Abstract

Coordination of large numbers of agents for performing complex tasks in complex domains is a rapidly progressing area of research. Because of the high complexity of the problem, approximate and heuristic algorithms are typically used for key coordination tasks. Such algorithms usually require tuning of algorithm parameters to get the best performance in particular circumstances. Manually tuning of parameters is sometime difficult. In this paper, we introduce a new concept of dynamic features for a neural network, called dynamic networks, to model the way a coordination algorithm will work under particular circumstances. Genetic algorithms are used to train the networks from an abstract simulation, TeamSim. At the end, the model is used to rapidly determine an appropriate configuration of the algorithm for a particular domain. Users specify required tradeoffs in algorithm performance and use the neural network to find the best configuration for those tradeoffs. Algorithm reconfiguration can even be performed online to improve the performance of an executing team as situation changes. We present preliminary results showing the approach promisingly facilitating users to configure and control a large team executing sophisticated teamwork algorithms.