Handling uncertainties in distributed constraint optimization problems using Bayesian inferential reasoning

In this paper, we propose the use of Bayesian inference and learning to solve DCOP in dynamic and uncertain environments. We categorize the agents Bayesian learning process into local learning or centralized learning. That is, the agents learn individually or collectively to make optimal predictions and share learning data. The agents' mission data is subjected to gradient descent or expectation-maximization algorithms for training purposes. The outcome of the training process is the learned network used by the agents for making predictions, estimations, and conclusions to reduce communication load. Surprisingly, results indicate that the algorithms are capable of producing accurate predictions using uncertain data. Simulation experiment result of a multiagent mission for wildfire monitoring suggest robust performance by the learning algorithms using uncertain data. We argue that Bayesian learning could reduce the communication load and improve DCOP algorithms scalability.