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Broad-XAI 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 operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) 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 the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. CXF is designed to incorporate many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes its decisions. This paper aims to: establish XRL as a distinct branch of XAI; introduce a conceptual framework for XRL; review existing approaches explaining agent behaviour; and identify opportunities for future research. Finally, this paper discusses how additional information can be extracted and ultimately integrated into models of communication, facilitating the development of Broad-XAI. [ABSTRACT FROM AUTHOR]

For an Artificially Intelligent (AI) system to maintain alignment between human desires and its behaviour, it is important that the AI account for human preferences. This paper proposes and empirically evaluates the first approach to aligning agent behaviour to human preference via an apologetic framework. In practice, an apology may consist of an acknowledgement, an explanation and an intention for the improvement of future behaviour. We propose that such an apology, provided in response to recognition of undesirable behaviour, is one way in which an AI agent may both be transparent and trustworthy to a human user. Furthermore, that behavioural adaptation as part of apology is a viable approach to correct against undesirable behaviours. The Act-Assess-Apologise framework potentially could address both the practical and social needs of a human user, to recognise and make reparations against prior undesirable behaviour and adjust for the future. Applied to a dual-auxiliary impact minimisation problem, the apologetic agent had a near perfect determination and apology provision accuracy in several non-trivial configurations. The agent subsequently demonstrated behaviour alignment with success that included up to complete avoidance of the impacts described by these objectives in some scenarios. [ABSTRACT FROM AUTHOR]