Bibliography

[1]

Mareike Picklum. Probabilistic action prospection based on experiences - representation, learning and reasoning in autonomous robotic agents. PhD thesis, University of Bremen, 2024. doi:10.26092/elib/2990.

[2]

Mareike Picklum and Michael Beetz. MatCALO: Knowledge-enabled machine learning in materials science. Computational Materials Science, 163:50 – 62, 2019. URL: http://www.sciencedirect.com/science/article/pii/S0927025619301296, doi:https://doi.org/10.1016/j.commatsci.2019.03.005.

[3]

Mihai Pomarlan, Daniel Nyga, Mareike Picklum, Sebastian Koralewski, and Michael Beetz. Deeper Understanding of Vague Instructions through Simulated Execution (Extended Abstract). In Proceedings of the 2017 International Conference on Autonomous Agents & Multiagent Systems, AAMAS '17. International Foundation for Autonomous Agents and Multiagent Systems, 2017.

[4]

Daniel Nyga, Mareike Picklum, Sebastian Koralewski, and Michael Beetz. Instruction Completion through Instance-based Learning and Semantic Analogical Reasoning. In International Conference on Robotics and Automation (ICRA). Singapore, 2017.

[5]

Daniel Nyga, Mareike Picklum, and Michael Beetz. What No Robot Has Seen Before – Probabilistic Interpretation of Natural-language Object Descriptions. In International Conference on Robotics and Automation (ICRA). Singapore, 2017.

[6]

Mareike Picklum, Daniel Nyga, Tom Schierenbeck, and Michael Beetz. Joint probability trees. 2023. URL: https://arxiv.org/abs/2302.07167, arXiv:2302.07167.