Automated cloud classification is severely bottlenecked by the need for massive, region-specific annotated datasets. This thesis investigates few-shot learning (FSL) approaches for the classification of mesoscale cloud formations across different geographic regions under limited data conditions.

To address this problem, the influence of varying amounts of additional data on generalisation to novel cloud regimes is evaluated. The study compares representation learning strategies, including self-supervised and supervised approaches, to assess their effectiveness in structuring the latent space for distinguishing cloud types. In parallel, two learning paradigms, transfer learning and episodic meta-learning, are analysed to determine how effectively they incorporate additional data when adapting to novel classes.

The results show that self-supervised learning is most effective in the strict few-shot regime, while supervised transfer learning makes the most effective use of additional data. In particular, Barlow Twins achieves the strongest performance under minimal data by avoiding reliance on noisy labels. When additional out-of-domain data, such as ImageNet and auxiliary cloud datasets, is introduced, supervised pre-training combined with transfer learning attains performance comparable to standard supervised learning, while requiring only a small number of labelled examples.

Most cooperative games are tackled by creating a team of agents who are optimised for each other and the problem. Creating an agent who can play in a variety of teams without any foreknowledge of its partner is a different challenge. These AI systems could useful for human-AI interaction as different people bring a lot of variance into the system. The training method synchronous K-level reasoning best response (SyKLRBR) tries to tackle this problem by creating agents based on grounded information. This research tested the potential of SyKLRBR in human-AI research evaluating the performance of its agent in the cooperative game Overcooked. The agents were able to obtain consistent scores against several unseen strategies, suggesting that SyKLRBR is able to create robust agents. Where SyKLRBR shows potential for medium cooperative settings this paper also discusses its great weakness for highly cooperative problems.