diff --git a/README.md b/README.md
index 29750db334142e74bd616e08c00e2e8fdb0fb68e..0244d42caad5f9b2937dcc338e5fab415891dd24 100644
--- a/README.md
+++ b/README.md
@@ -1,23 +1,11 @@
-# Triggering Proactive Business Process Adaptations via Online Reinforcement Learning
-by Andreas Metzger, Tristan Kley, and Alexander Palm
+# Feature-Model-Guided Online Reinforcement Learning for Self-Adaptive Services
+by Andreas Metzger, Clément Quinton, Zoltan Adam Mann, Luciano Baresi, and Klaus Pohl 
 
-For the sake of transparency, reproducibility, and replicability, below we provide the links to the artefacts created as part of the work on our BPM 2020 submission in order to facilitate reproducibility and replicability.
+Below we provide the links to the artefacts created as part of the work on our ICSOC 2020 submission.
 
-## Code
+* Code realizing the learning strategies and their integration into Q-Learning: [https://github.com/Trikley/proactive_BPM_adaptation](https://github.com/Trikley/proactive_BPM_adaptation)
 
-* Code to train and evaluate the RNN-LSTM ensembles: [https://github.com/Chemsorly/BusinessProcessOutcomePrediction](https://github.com/Chemsorly/BusinessProcessOutcomePrediction)
+* Data set including configurations, rewards and QoS forthe cloud resource management service CloudRM, used as experimental subject: [https://uni-duisburg-essen.sciebo.de/s/iFy6y0BsAWTgWLV](https://uni-duisburg-essen.sciebo.de/s/iFy6y0BsAWTgWLV)
 
-* Code to generate predictions and reliability estimates, linked to the RL algorithm: [https://github.com/Trikley/proactive_BPM_adaptation](https://github.com/Trikley/proactive_BPM_adaptation)
-
-## Data Sets
-We used the real-world data sets Traffic, BPIC2012 and BPIC2017 for experimental evaluation
-
-* Original source: [https://data.4tu.nl/repository/](https://data.4tu.nl/repository/)
-* Transformed into the input for RNN-LSTM models: [https://uni-duisburg-essen.sciebo.de/s/zpX4kRXkgcX3Ez4](https://uni-duisburg-essen.sciebo.de/s/zpX4kRXkgcX3Ez4)
-
-* Prediction results for all RNN-LSTM models of the ensemble: [https://uni-duisburg-essen.sciebo.de/s/iFy6y0BsAWTgWLV](https://uni-duisburg-essen.sciebo.de/s/iFy6y0BsAWTgWLV)
-
-## Experimental Results
-
-* Available from: [https://uni-duisburg-essen.sciebo.de/s/6JyqueaMBQmHMXt](https://uni-duisburg-essen.sciebo.de/s/6JyqueaMBQmHMXt)
+* Experimental results, including rewards and QoS over time: [https://uni-duisburg-essen.sciebo.de/s/6JyqueaMBQmHMXt](https://uni-duisburg-essen.sciebo.de/s/6JyqueaMBQmHMXt)