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Found 8 papers, 2 papers with code
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire.
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.
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.
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.
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.
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.
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front.
Variance of ML-based software fault predictors: are we really improving fault prediction?
These stochastic elements, also known as nondeterminism-introducing (NI) factors, lead to variance in the training process and as a result, lead to variance in prediction accuracy and training time.
