Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is not guaranteed to do so and Weighted Expected Improvement does so only for a restricted range of weights. We introduce two novel ϵ-greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory, and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that ϵ-greedy algorithms are generally at least as effective as conventional acquisition functions (e.g. EI and UCB), particularly with a limited budget. In higher dimensions ϵ-greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real world computational fluid dynamics optimisation problem and a robotics active learning problem. Our analysis and experiments suggest that the most effective strategy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution

Scientists’ warning on affluence

For over half a century, worldwide growth in affluence has continuously increased resource use and pollutant emissions far more rapidly than these have been reduced through better technology. The affluent citizens of the world are responsible for most environmental impacts and are central to any future prospect of retreating to safer environmental conditions. We summarise the evidence and present possible solution approaches. Any transition towards sustainability can only be effective if far-reaching lifestyle changes complement technological advancements. However, existing societies, economies and cultures incite consumption expansion and the structural imperative for growth in competitive market economies inhibits necessary societal change

Towards Learning of Safety Knowledge from Human Demonstrations

Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, a new kind of hazards being implicated by the robot’s capability to manipulate the environment occurs: hazardous environmental object interactions. One of the open questions in safety research is integrating safety knowledge into robotic systems, enabling these systems behaving safety-conscious in hazardous situations. In this paper a safety procedure is described, in which learning of safety knowledge from human demonstration is considered. Within the procedure, a task is demonstrated to the robot, which observes object-to-object relations and labels situational data as commanded by the human. Based on this data, several supervised learning techniques are evaluated used for finally extracting safety knowledge. Results indicate that Decision Trees allow interesting opportunities

Robust Exploration/Exploitation Trade-Offs in Safety-Critical Applications

With regard to future service robots, unsafe exceptional circumstances can occur in complex systems that are hardly to foresee. In this paper, the assumption of having no knowledge about the environment is investigated using reinforcement learning as an option for learning behavior by trial-and-error. In such a scenario, action-selection decisions are made based on future reward predictions for minimizing costs in reaching a goal. It is shown that the selection of safetycritical actions leading to highly negative costs from the environment is directly related to the exploration/exploitation dilemma in temporal-di erence learning. For this, several exploration policies are investigated with regard to worst- and best-case performance in a dynamic environment. Our results show that in contrast to established exploration policies like epsilon-Greedy and Softmax, the recently proposed VDBE-Softmax policy seems to be more appropriate for such applications due to its robustness of the exploration parameter for unexpected situations

Conceptual Design of a Dynamic Risk-Assessment Server for Autonomous Robots

Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, there is a new kind of hazards being implicated by the robot’s capability to manipulate the environment: Hazardous environmental object interactions. Therefore, the realization of the Dynamic Risk-Assessment approach with special scope on object-interaction risks is addressed in this paper. A server-based architecture is proposed facilitating a feasible integration into robotic systems and realization of software and hardware redundancy as well

From Solar Cells to Ocean Buoys: Wide-Bandwidth Limits to Absorption by Metaparticle Arrays

In this paper, we develop an approximate wide-bandwidth upper bound to the absorption enhancement in arrays of metaparticles, applicable to general wave-scattering problems and motivated here by ocean-buoy energy extraction. We show that general limits, including the well-known Yablonovitch result in solar cells, arise from reciprocity conditions. The use of reciprocity in the stochastic regime leads us to a corrected diffusion model from which we derive our main result: an analytical prediction of optimal array absorption that closely matches exact simulations for both random and optimized arrays under angle and frequency averaging. This result also enables us to propose and quantify approaches to increase performance through careful particle design and/or using external reflectors. We show, in particular, that the use of membranes on the water's surface allows substantial enhancement. ©2019 American Physical Society.Army Research Office - Cooperative Agreement (W911NF-18-2-0048