Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domainexpert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency 1 arXiv:1805.04220v1 [cs.AI] of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.
The distributed nature of wireless sensor networks illustrates well classical engineering tradeoffs: how to minimize communication (and possibly computation) cost, and thus energy dissipation, while maintaining acceptable performance levels in estimation and inference applications. We study a simple sensor network under dependent Gaussian noise and develop strategies for parameter estimation in a variety of communication scenarios. From an energy point of view, sending all data to a fusion center is the most costly, but leads to optimum performance results. Processing data at each sensor and sending parameter estimates and associated quality measures is a reasonable communication saving procedure and yet, in some cases, may lead to performance equivalent to sending all data to the fusion center. A sequential procedure is most parsimonious in terms of communication cost and especially effective in large wireless sensor networks. We explore those conditions for which little, or no loss in performance is encountered with this sequential procedure. Specifically, we provide analytical expressions for the maximum likelihood estimator under "geometric" dependent noise. We show, by means of analysis and simulations, that the performance is only marginally degraded when the noise is assumed to be independent.
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