This study explores the application of supervised and unsupervised machine learning algorithms for predicting the sex of sheep using measurements of the talus bone in archaeozoological research. Leveraging data from well-documented sheep populations, we trained and tested various machine learning algorithms, such as kNN, SVMs, Decision Trees, Neural Networks, k-Means, DBSCAN, and GMM – demonstrating high accuracy in sex classification across multiple datasets from various time periods. We furthermore evaluate a variety of clustering results on unlabeled data and highlight their respective strengths and drawbacks. Our results suggest that machine learning offers a promising direction for enhancing the analysis of ancient and recent animal remains, providing valuable insights into past animal husbandry practices and their implications for understanding human history.

We tasked two supervised keypoint detection networks to automatically identify anatomically relevant landmarks on sheep bones. The results of the models were compared to manual labeling by a domain expert, yielding satisfactory initial results that provide a promising foundation for further research.

Recent successes in AI research concerning traditional games like GO, have led to increased interest in the field of reinforcement learning. Modern board game design, however, has risen in complexity. This paper introduces a novel task for reinforcement learning: “Quacks of Quedlinburg”. A modern board game with risk management, deck building, and the option to choose a specific rule set out of thousands of possible combinations for every game. We provide an environment based on the game and perform initial experiments. In these, we found that Deep Q-Learning agents can significantly outperform simple heuristics.