Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential equations, and set-theoretic notation, have been used as the main tools. The outcome is a model of a game-based optimization process in a two-element logistics system and an algorithm applied to find optimal steering strategies. The algorithm has been initially verified with the use of simulation based on a Bayesian network (BN) and a structured set of possible strategies (OP/TO) calculated with the use of QGeNie Modeller, finally prepared for Python. It has been proved the algorithm at this stage has no deadlocks and unforeseen loops and is ready to be challenged with the original big set of learning data from a drone-operating company (as the next stage of the planned research).

中文翻译：

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使用机器学习管理电子商务物流过程中操作员与技术对象关系的基于游戏的系统算法

机器学习 (ML) 利用创新技术（例如，在电子商务中使用无人机进行自动交付）应用于各种物流流程。早期的挑战表明，由于缺乏控制算法的理论基础，无人机的转向能力和认知差距不足。本文的目的是基于博弈是面向物流的假设，并且该算法支持ML在电子商务优化管理中的应用。代数方法，包括矩阵、拉格朗日函数、微分方程组和集合论符号，已被用作主要工具。结果是二元物流系统中基于博弈的优化过程模型和用于寻找最佳转向策略的算法。该算法已通过使用基于贝叶斯网络 (BN) 的模拟和使用 QGeNie Modeller 计算的一组结构化的可能策略 (OP/TO) 进行了初步验证，最终为 Python 做好了准备。已经证明现阶段的算法没有死锁和不可预见的循环，并准备好接受来自无人机运营公司的原始大学习数据集的挑战（作为计划研究的下一阶段）。该算法已通过使用基于贝叶斯网络 (BN) 的模拟和使用 QGeNie Modeller 计算的一组结构化的可能策略 (OP/TO) 进行了初步验证，最终为 Python 做好了准备。已经证明现阶段的算法没有死锁和不可预见的循环，并准备好接受来自无人机运营公司的原始大学习数据集的挑战（作为计划研究的下一阶段）。该算法已通过使用基于贝叶斯网络 (BN) 的模拟和使用 QGeNie Modeller 计算的一组结构化的可能策略 (OP/TO) 进行了初步验证，最终为 Python 做好了准备。已经证明现阶段的算法没有死锁和不可预见的循环，并准备好接受来自无人机运营公司的原始大学习数据集的挑战（作为计划研究的下一阶段）。