KEYWORDS

1. Introduction

2. Explainable AI (XAI) and XAR  
2.1. XAI  
2.2. Explainable AI Planning (XAIP)  
2.3. Explainable Reinforcement Learning (XRL)  
2.4. XAR  

3. What is the explainability of autonomous robots?  
3.1. What is an explanation?  
3.2. Preparation of defining explainability  
3.3. Definition of explanation in XAR  
3.4. Owning an interpretable decision-making space  
3.5. Estimation of the model of others  
3.6. Estimation of the information needed for a user to estimate  
3.7. Presentation to user of explanation  

4. Survey on key issues in XAR and associated research  
4.1. Autonomous acquisition of interpretable decision-making space  
4.2. Estimation of human decision-making space and policy  
4.3. Extraction of key elements in the process  
4.4. Verbalization and visualization of explanations  
4.5. Other research studies  

5. Future research issues  
5.1. Estimation of decision-making space and planning algorithm of others  
5.2. Consideration of the social aspects of explanation  
5.3. Application to real-world problems  
5.4. Application to interactions  
5.5. Ethical issues with generating explanations that are untrue  

6. Conclusions