Virtue integrated platform : holistic support for distributed ship hydrodynamic design

Ship hydrodynamic design today is often still done in a sequential approach. Tools used for the different aspects of CFD (Computational Fluid Dynamics) simulation (e.g. wave resistance, cavitation, seakeeping, and manoeuvring), and even for the different levels of detail within a single aspect, are often poorly integrated. VIRTUE (the VIRtual Tank Utility in Europe) project has the objective to develop a platform that will enable various distributed CFD and design applications to be integrated so that they may operate in a unified and holistic manner. This paper presents an overview of the VIRTUE Integrated Platform (VIP), e.g. research background, objectives, current work, user requirements, system architecture, its implementation, evaluation, and current development and future work

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

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 domain-expert 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 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.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table