Models of Intervention: Helping Agents and Human Users Avoid Undesirable Outcomes

Abstract

When working in an unfamiliar online environment, it can be helpful to have an observer that can intervene and guide a user toward a desirable outcome while avoiding undesirable outcomes or frustration. The Intervention Problem is deciding when to intervene in order to help a user. The Intervention Problem is similar to, but distinct from, Plan Recognition because the observer must not only recognize the intended goals of a user but also when to intervene to help the user when necessary. We formalize a family of Intervention Problems and show that how these problems can be solved using a combination of Plan Recognition methods and classification algorithms to decide whether to intervene. For our benchmarks, the classification algorithms dominate three recent Plan Recognition approaches. We then generalize these results to Human-Aware Intervention, where the observer must decide in real time whether to intervene human users solving a cognitively engaging puzzle. Using a revised feature set more appropriate to human behavior, we produce a learned model to recognize when a human user is about to trigger an undesirable outcome. We perform a human-subject study to evaluate the Human-Aware Intervention. We find that the revised model also dominates existing Plan Recognition algorithms in predicting Human-Aware Intervention.

An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence

“…Regarding Frontiers in Artificial Intelligence, we notice that the Matthews correlation coefficient was employed by the authors of three original research studies (Bhatt et al, 2021;Li et al, 2021;Wu et al, 2021), two methods articles (Fletcher et al, 2021;Weerawardhana et al, 2022), and one review (Tripathi et al, 2021). The study of Li et al (2021) presents a deep learning application on chemoinformatics data for the prediction of carcinogenicity.…”

“…Fletcher et al (2021), instead, present a study on fairness in artificial intelligence applied to public health, reporting a case study on machine applied to data of pulmonary disease. Weerawardhana et al (2022) employed the MCC to measure the results in a human-aware intervention and behavior classification study, In their review article, Tripathi et al (2021) reported some AI best practices in manufacturing, indicating the MCC as one of the confusion matrix rates employed in this field.…”