Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.
翻译:Automaton (LA) 是一种适应性的自我组织模型, 通过与未知环境的相互作用来改进行动选择。 具有有限动作的LA可以分为两大类: 固定和可变结构。 此外, 变量动作设置学习自动马顿( VASLA) 是变量结构学习自动马顿( Automaton) 的主要子集之一 。 在本文中, 我们提议VDHLA, 一种新型混合学习自动马顿模式, 是固定结构学习自动马顿的组合。 在拟议模型中, 可变动作设置学习自动马顿可以增加、 减少或保持固定结构学习自动马顿的深度不变。 在行动转换阶段, 固定结构学习自动马顿可以分为两大类: 固定和可变结构学习自动马顿。 此外, 变量行动模式的深度可以在交错结构结构( SVDHLA) 或不对称方式( AVDHLA) 中变化。 根据我们所知, 这是第一个明智的混合模型, 可以改变固定结构学习自动马达。 一些计算机模拟模拟, 来研究拟议模型的模型的性模型的性模型在固定和非静止环境中, 将SL 将SL 与SL 运行的运行中的SL 。 与SL 显示的运行的模型与SL 。 运行的运行的模型与SL 显示的运行中的SL 与SB 。 。 。 显示的模型与SL 。 。 。 显示的模型与SL 。