A Deep Reinforcement Learning Framework with Formal Verification

Abstract

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.