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

Further education, work-based learning and community learning in Wales, 2011/12 (provisional figures)

• The figures in this release show a decline in overall learner numbers between 2010/11 and 2011/12 (continuing since 2005/06) but with variations between the individual sectors and age groups. • In 2011/12 there were 226,575 distinct learners at FE Institutions, Community Learning (CL) or Work-based Learning (WBL) providers, 5.7 per cent lower than in 2010/11. • Total numbers at FE institutions fell by 4.4 per cent, with full-time learner numbers almost unchanged (0.2 per cent fall) and part-time learner numbers at FEIs 5.2 per cent lower. • Learners in local authority community learning fell by 10 per cent, relative to 2010/11. • For WBL provision, learner numbers on a full year basis fell by 6.4 per cent though the number of trainees in learning on the last day of the year was 6.2 per cent higher than at 31 July 2011 (partly because of day of week effects), the latter date having immediately preceded a change in the structure of Welsh Government support to WBL providers