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Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring
This means that acting on less accurate predictions may lead to unnecessary adaptations or missed adaptations.
A User Study on Explainable Online Reinforcement Learning for Adaptive Systems
However, Online RL requires the definition of an effective and correct reward function, which quantifies the feedback to the RL algorithm and thereby guides learning.
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems
Online reinforcement learning, i. e., employing reinforcement learning (RL) at runtime, is an emerging approach to realizing self-adaptive systems in the presence of design time uncertainty.
Counterfactual Explanations for Predictive Business Process Monitoring
We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models.
Feature-Model-Guided Online Learning for Self-Adaptive Systems
Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process.
