arXiv:2403.19760v1 Announce Type: new
Abstract: As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption. Explainable Artificial Intelligence (XAI) aims to reduce confusion and foster trust in systems by providing explanations of agent behavior. Partially observable Markov decision processes (POMDPs) provide a flexible framework capable of reasoning over transition and state uncertainty, while also being amenable to explanation. This work investigates the use of user-provided counterfactuals to generate contrastive explanations of POMDP policies. Feature expectations are used as a means of contrasting the performance of these policies. We demonstrate our approach in a Search and Rescue (SAR) setting. We analyze and discuss the associated challenges through two case studies.
Introduction:
The increasing reliance on autonomous systems has raised concerns about the need for transparency and accountability. When it comes to Artificial Intelligence (AI), Explainable AI (XAI) has emerged as a crucial field that aims to provide explanations for the behavior of AI systems. In this context, this research paper explores the use of user-provided counterfactuals to generate contrastive explanations of policies in Partially Observable Markov Decision Processes (POMDPs).
Partially Observable Markov Decision Processes (POMDPs)
POMDPs provide a flexible framework for modeling probabilistic systems with uncertainty in transition and states. They allow AI agents to reason over incomplete information and make decisions based on their observations. With the ability to handle uncertain environments, POMDPs are well-suited for generating explanations in XAI.
User-Provided Counterfactuals for Contrastive Explanations
This study explores the use of user-provided counterfactuals as a means of generating contrastive explanations in POMDP policies. By presenting alternative scenarios to users, the researchers aim to illustrate how the AI system would have performed if certain variables had been different.
The researchers propose using feature expectations to quantify and contrast the performance of different policies. By comparing these feature expectations, users can gain insights into the effectiveness of different decision-making strategies employed by the AI agent. This approach enhances the interpretability of POMDP policies and promotes a deeper understanding of the AI system’s behavior.
Application in Search and Rescue (SAR) Setting:
The researchers demonstrate their approach in a Search and Rescue (SAR) setting. This application is highly relevant, as decision-making in SAR scenarios is especially critical and can have significant consequences on human lives. By providing contrastive explanations, the AI system can help users understand why certain decisions were made and evaluate the effectiveness of different policies in different situations.
Challenges and Future Directions:
This work brings forth several challenges related to generating contrastive explanations in POMDP policies. Some of these challenges include handling high-dimensional feature spaces, incorporating user preferences into the explanations, and efficiently computing feature expectations.
In the future, research in this area could benefit from a multi-disciplinary approach. Collaborating with experts from fields such as psychology, cognitive science, and human-computer interaction would provide valuable insights into how humans perceive and understand contrastive explanations. Additionally, addressing the challenges mentioned earlier would require innovations in algorithms, data representation, and user interface design.
In conclusion, this research paper highlights the significance of XAI in promoting transparency and trust in autonomous systems. By leveraging user-provided counterfactuals, contrastive explanations can be generated for POMDP policies, allowing users to better understand and evaluate the behavior of AI agents. The application of this approach in a SAR setting demonstrates its practical relevance. However, further research is needed to address the challenges and explore the potential of multi-disciplinary collaborations in this field.