Broad Explainable Artificial Intelligence moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machine learning algorithms into a coherent explanation of an agent's behaviour that is aligned to the communication needs of the explainee. Reinforcement Learning (RL) methods, we propose, provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. However, these algorithms all operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) is relatively recent field of research that aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives. This paper aims to introduce a conceptual framework, called the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. Additionally, we recognise that RL methods have the ability to incorporate a range of technologies to allow agents to adapt to their environment. CXF is designed for the incorporation of many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes and justify its decisions.
翻译:我们提议,强化学习(RL)方法为开发宽度XAI所需的认知模式提供了潜在的骨干。RL是一套在解决一系列相继决策问题方面越来越成功的方法。然而,这些算法都作为黑箱问题解答器运作,通过一系列复杂的价值和功能,模糊其决策政策,从而模糊其决策政策。可扩展的RL(XRL)是相对近期的研究领域,目的是开发从代理人的传播需求中提取概念的技术:环境观;内在/外部动机/保险;Q-价值、目标和目的。本文旨在引入一个概念框架,称为Causal XRL(CXF)框架(CXF),将当前的XRL(X)研究和RL(RL)作为当前X(X)研究和使用RL(RL)系统作为基础,以便把其一体化技术纳入到宽度-XAI(RL)系统的发展。