Hot electron and x-ray production from solid targets coated with polystyrene-spheres which are irradiated with high-contrast, 100fs, 400nm light pulses at intensity up to 2×1017W∕cm2 have been studied. The peak hard x-ray signal from uncoated fused silica targets is an order of magnitude smaller than the signal from targets coated with submicron sized spheres. The temperature of the x-rays in the case of sphere-coated targets is twice as hot as that of uncoated glass. A sphere-size scan of the x-ray yield and observation of a peak in both the x-ray production and temperature at a sphere diameter of 0.26μm, indicate that these results are consistent with Mie enhancements of the laser field at the sphere surface and multipass stochastic heating of the hot electrons in the oscillating laser field. These results also match well with particle-in-cell simulations of the interaction.
Planning issues in a continuous domain in the presence of noise lead to important modeling and computational difficulties. The game of billiards has offered many interesting challenges to both communities of AI and Optimization. We present a two-layered approach consisting in a high level planner and a low level controller. We propose here a refined controller for billiards based on robust optimization combined with specific adjustments to take advantage of the domain knowledge. A multi-objective formulation of a robust controller will be presented to provide the tools needed to execute any desired shot on the table, as part of a two-layered approach for the game of billiards. Some results will be then shown, followed by a short discussion on future work.
In the past , we have proposed a two-layered approach to compute a winning strategy for the game of Billiards. AI tools as well as robust optimization routines for noisy environments were combined to plan the sequence of shots. We complete the modeling here by introducing significant developments for the highlevel planner which guides the precise optimal controller to generate a plan given at any random initial state. We will first resume the general model for this particular class of problems and then propose several domain-specific heuristics to guide our search and render the problem tractable. Several improvements to the optimal robust controller, including refinements in the objective function, will also be presented in order to improve single-shot optimization. Results are presented demonstrating the full potential of the methods proposed making it the state of the art in regards to the game of Billiards.
In this work, we take a closer look at the difficulties inherent to the creation of an artificial intelligence (AI) for the game of Straight billiards (14.1 continuous). We begin by establishing the key components that make this variant of billiards interesting in regard to past work on the game of eight-ball. We then address each of these components by decomposing the problem into two aspects: optimal control and planning. A new model for the optimal control of the cue ball to break clusters in between games is presented, as well as a model for the execution of defensive shots. We follow with a short discussion on the importance of planning carefully when only a few balls remain on the table and propose a planning approach based on an analysis of the table state to select the sequence of balls to pocket on the table. Results are finally presented and analyzed, followed by a discussion on future work.
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