In this paper, we focus on the problem of learning a Bayesian network over distributed data stored in a commodity cluster. Specifically, we address the challenge of computing the scoring function over distributed data in a scalable manner, which is a fundamental task during learning. We propose a novel approach designed to achieve: (a) scalable score computation using the principle of gossiping; (b) lower resource consumption via a probabilistic approach for maintaining scores using the properties of a Markov chain; and (c) effective distribution of tasks during score computation (on large datasets) by synergistically combining well-known hashing techniques. Through theoretical analysis, we show that our approach is superior to a MapReduce-style computation in terms of communication bandwidth. Further, it is superior to the batch-style processing of MapReduce for recomputing scores when new data are available.

Operational concepts in which a single operator teams with multiple autonomous vehicles are now considered feasible due to advances in automation technology. This will require that an operator be able to express a high-level intent, or goal, to the vehicle team rather than direct the actions of individual assets. Successful operator-autonomy collaboration must quickly capture the operator's intent and then portray the autonomy's trade-offs between different courses of action in an intuitive interface. This paper describes how a multi-disciplinary effort was employed in the design of a display that highlights the trade-off of autonomy-generated plans and supports the efficient allocation of assets to surveillance tasks. Our novel control station approach combines domain modeling and multi-objective optimization with innovative interfaces to enable a single operator to effectively command a team of unmanned vehicles.

Plan monitoring in a collaborative multi-agent system requires an agent to not only monitor the execution of its own plan, but also to detect possible deviations or failures in the plan execution of its teammates. In domains featuring partial observability and uncertainty in the agents’ sensing and actuation, especially where communication among agents is sparse (as a part of a cost-minimized plan), plan monitoring can be a significant challenge. We design an Expectation Maximization (EM) based algorithm for detection of plan deviation of teammates in such a multi-agent system. However, a direct implementation of this algorithm is intractable, so we also design an alternative approach grounded on the agents’ plans, for tractability. We establish its equivalence to the intractable version, and evaluate these techniques in some challenging tasks.