Conflicts resolution and situation awareness in heterogeneous multi-agent missions using publish-subscribe technique and inferential reasoning

In this paper, we propose a priority-based publish-subscribe approach to tackle reasoning in beliefs conflicts for a heterogeneous multi-agent mission. Agents subscribe to other agents' topics and rank them based on agents' situation awareness. Bayesian Belief Network (BBN) was used in maintaining agents' belief and recorded mission information could be used for the BBN training using conjugate gradient descent or expectation-maximization algorithms. The output of the training is the learned network for agents' predictions, estimations, and conclusions. We also propose an agent's self presumption inferential reasoning where agents learned heuristics and used them for future inferences. We test the system by using a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with different sensor profiles and capacities tasked together to perform forest fire searching. To verify belief and settle conflicts, agents follow these steps: sequentially assess the prioritized publish-subscribe topics, inferential reasoning using the learned network, inferential reasoning using logical propositions, and learning process. From our experiment, the BBN training and prediction perfection grow up with the increase in the number of training data. Future work focuses on obtaining the optimal number of samples needed for effective prediction, effective agents' beliefs merging, communication protocol, and bandwidth utilization.