# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 5 Models and Methods to Ensure and Assess AIS Resilience
### Graceful Degradation  
Adversarial attacks, fault injections, concept drifts, and out-of-distribution examples cannot always be absorbed, so the development of graceful degradation remains relevant [2; 6]. Table 5 summarizes the three most well-known approaches to ensuring graceful degradation of AIS: implementing prediction granularity, Zero-Shot Learning and Switching between models or branches. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Capability** & **Weakness** & **Methods and Algorithms** \\ \hline \multirow{4}{*}{Prediction granularity (hierarchical prediction)} & Using confident coarse-grained prediction instead of low-confident fine-grained prediction & Approach efficiency depends on architectural solution, data balanciness for each hierarchical level and response design for coarse-grained prediction. & Nested Learning for Multi-Level Classification [108]. \\ \cline{1-1} \cline{2-4}  & Ability to recognize samples whose categories may not have been seen at training & Not reliable enough due to hubness in semantic space and projection domain shift problem. & Embedding-based methods [110]. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Approaches and algorithms to ensure the graceful degradation. Prediction granularity consists in the implementation of hierarchical decision-making [108; 109]. If the prediction at the lowest hierarchical level is not sufficiently confident, then the AIS should favor a highly confident prediction at a higher hierarchical level. In this case, the response design should be provided for processing high-level coarse-grained prediction. In [108], a hierarchical classification is proposed, where superclasses are predicted on lower layers of the neural network, and fine-grained predictions are on high-level layers of the neural network. In [109], a hierarchical image classification combined with multi-resolution recognition is proposed to simplify the task of recognizing more abstract classes that are recognized on images with lower resolution. Zero-shot learning aims to build AI-models which can classify objects of unseen classes (target domain) via transferring knowledge obtained from other seen classes (source domain) with the help of semantic information. Semantic information bridges the gap between the seen and unseen classes by embedding the names of both seen and unseen classes in high-dimensional vectors from a shared embedding space. Pragmatic version of Zero-Shot learning recognizes samples from both seen and unseen classes [110]. This version of Zero-Shot learning is called Generalized Zero-Shot learning. Generalized Zero-Shot learning methods can be broadly categorized into Embedding-based methods and Generative-based methods. Embedding-based methods involve learning an embedding space to associate the low-level features of seen classes with their corresponding semantic vectors [110]. The learned projection function is used to recognize novel classes by measuring the similarity score between the prototype representations and predicted representations of the data samples in the embedding space. Out-of-distribution detector is needed to separate the seen class instances from those of the unseen classes. Generative-based methods involve training a model to generate examples or features for the unseen classes based on the samples of seen classes and semantic representations of both classes. Generated samples for unseen classes can be used by conventional supervised learning to update the AI-model. In addition, Zero-shot learning has spread beyond classification tasks to regression and reinforcement learning tasks. Unlike current reinforcement learning agents, a zero-shot reinforcement learning agent should solve any reinforcement learning task in a given environment, instantly with no additional planning or learning [115].