Temporal Counterfactual Explanations of Behaviour Tree Decisions
Tamlin Love, Antonio Andriella, Guillem Aleny\`a

TL;DR
This paper introduces a novel method for generating causal, counterfactual explanations for robot decisions driven by behaviour trees, enhancing transparency and trustworthiness.
Contribution
It presents an automatic approach to build causal models from behaviour trees and domain knowledge to generate diverse counterfactual explanations in real time.
Findings
Successfully explains a wide range of behaviour tree decisions.
Operates in real time, unlike previous methods.
Provides consistent and accurate causal explanations.
Abstract
Explainability, in particular, the ability for robots to explain why they have made a decision or behaved in a certain way, is a critical tool in helping users understand the robots they interact and coexist with. Behaviour trees are a popular framework for controlling the decision-making of robots, and thus a natural question to ask is whether or not a system driven by a behaviour tree is capable of answering "why" questions. While explainability for behaviour tree-driven robots has seen some prior attention, no existing methods are capable of generating causal, counterfactual explanations which detail the reasons for robot decisions and behaviour. Therefore, in this work, we introduce a novel approach which automatically generates counterfactual explanations in response to contrastive "why" questions. Our method achieves this by first automatically building a causal model from the…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Bayesian Modeling and Causal Inference · Multimodal Machine Learning Applications
