Artificial Intelligence (AI) and data are reshaping organizations and businesses. Human Resources (HR) management and talent development make no exception, as they tend to involve more automation and growing quantities of data. Because this brings implications on workforce, career transparency, and equal opportunities, overseeing what fuels AI and analytical models, their quality standards, integrity, and correctness becomes an imperative for those aspiring to such systems. Based on an ontology transformation to B-machines, this article presents an approach to constructing a valid and error-free career agent with Deep Reinforcement Learning (DRL). In short, the agent's policy is built on a framework we called Multi State-Actor (MuStAc) using a decentralized training approach. Its purpose is to predict both relevant and valid career steps to employees, based on their profiles and company pathways (observations). Observations can comprise various data elements such as the current occupation, past experiences, performance, skills, qualifications, and so on. The policy takes in all these observations and outputs the next recommended career step, in an environment set as the combination of an HR ontology and an Event-B model, which generates action spaces with respect to formal properties. The Event-B model and formal properties are derived using OWL to B transformation.

人工智能 (AI) 和数据正在重塑组织和企业。人力资源 (HR) 管理和人才开发也不例外，因为它们往往涉及更多的自动化和越来越多的数据。由于这对劳动力、职业透明度和平等机会产生了影响，因此监督人工智能和分析模型的燃料、它们的质量标准、完整性和正确性成为那些渴望使用此类系统的人的当务之急。基于对 B 机的本体转换，本文提出了一种使用深度强化学习 (DRL) 构建有效且无错误的职业代理的方法。简而言之，智能体的策略是建立在我们称为多状态参与者 (MuStAc) 的框架之上的，该框架使用分散式训练方法。其目的是根据员工的个人资料和公司路径（观察）预测相关且有效的职业发展步骤。观察可以包括各种数据元素，例如当前职业、过去的经验、绩效、技能、资格等。该政策接受所有这些观察并输出下一个推荐的职业步骤，在一个 HR 本体和事件 B 模型组合的环境中，该模型生成关于正式属性的动作空间。Event-B 模型和形式属性是使用 OWL 到 B 的转换导出的。该政策接受所有这些观察并输出下一个推荐的职业步骤，在一个 HR 本体和事件 B 模型组合的环境中，该模型生成关于正式属性的动作空间。Event-B 模型和形式属性是使用 OWL 到 B 的转换导出的。该政策接受所有这些观察并输出下一个推荐的职业步骤，在一个 HR 本体和事件 B 模型组合的环境中，该模型生成关于正式属性的动作空间。Event-B 模型和形式属性是使用 OWL 到 B 的转换导出的。