论文标题
lambda：通过搜索空间量化覆盖黑盒不平等的解决方案集
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization
论文作者
论文摘要
Black-Box功能广泛用于建模复杂的问题，这些问题不提供明确的信息，而是输入和输出。尽管现有的黑框功能优化研究，但在许多实际情况下，解决方案设置满足了黑框功能的不等式的功能比最佳的作用更重要。在本文中，通过有限的评估覆盖了通过有限的评估来覆盖解决方案的解决方案。我们在基于样本的搜索范式中正式化了这个问题，并通过混乱矩阵分析构建了覆盖标准。此外，我们提出了Lambda（具有密度适应的潜在蒙特卡洛束搜索）来解决BBC问题。 Lambda可以通过将搜索空间递归将搜索空间递归到被公认和拒绝的子空间中，可以快速集中在解决方案周围。与LA-MCT相比，Lambda引入了密度信息，以克服优化的采样偏差并获得更多探索。基准测试表明，Lambda在所有基础线中都取得了最先进的表现，并且比随机搜索最多要获得95％的覆盖率。实验还表明，Lambda在虚拟测试中对自主系统的验证有着有希望的未来。
Black-box functions are broadly used to model complex problems that provide no explicit information but the input and output. Despite existing studies of black-box function optimization, the solution set satisfying an inequality with a black-box function plays a more significant role than only one optimum in many practical situations. Covering as much as possible of the solution set through limited evaluations to the black-box objective function is defined as the Black-Box Coverage (BBC) problem in this paper. We formalized this problem in a sample-based search paradigm and constructed a coverage criterion with Confusion Matrix Analysis. Further, we propose LAMBDA (Latent-Action Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA can focus around the solution set quickly by recursively partitioning the search space into accepted and rejected sub-spaces. Compared with La-MCTS, LAMBDA introduces density information to overcome the sampling bias of optimization and obtain more exploration. Benchmarking shows, LAMBDA achieved state-of-the-art performance among all baselines and was at most 33x faster to get 95% coverage than Random Search. Experiments also demonstrate that LAMBDA has a promising future in the verification of autonomous systems in virtual tests.
