The resolution of observations is significantly larger in the ViZDoom environment, and there are more possible actions. In the ViZDoom environment, PLANS has access to more demonstrations to infer the underlying program. For additional information about the datasets, we refer to Sun et al. (2018). Figures 4 and 5 give examples of programs and demonstrations for the Karel and ViZdoom benchmarks respectively. In C.2, we detail which heuristics were used to improve the program and sequenceaccuracymetric. Results are reported in Table 5.

Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions.

However,in manyapplications there are several different parameters one might wish to vary: for example, scale and density. In contrast to the one-parameter setting, techniques for applying statistics and machine learning in the setting of multiparameter persistence are not well understood due to the lack of a concise representationoftheresults.

Collaborative inference leverages diverse features provided by different agents (e.g.,sensors)formoreaccurateinference. Acommonsetupiswhereeachagent sends its embedded features instead of the raw data to the Fusion Center (FC) for joint prediction. In this setting, we consider inference phase attacks when asmall fraction of agents is compromised.