- Title
- Explainable reinforcement learning for broad-XAI: a conceptual framework and survey
- Creator
- Dazeley, Richard; Vamplew, Peter; Cruz, Francisco
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197200
- Identifier
- vital:18813
- Identifier
-
https://doi.org/10.1007/s00521-023-08423-1
- Identifier
- ISSN:0941-0643 (ISSN)
- Abstract
- 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. © 2023, The Author(s).
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Neural Computing and Applications Vol. 35, no. 23 (2023), p. 16893-16916
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 The Author(s)
- Rights
- Open Access
- Subject
- 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; 4611 Machine learning; Broad XAI; Explainable artificial intelligence (XAI); Explainable Reinforcement learning (XRL); Reinforcement learning (RL)
- Full Text
- Reviewed
- Funder
- Open Access funding enabled and organized by CAUL and its Member Institutions.
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