Automated Driving

Better AI through logical Scaffolding


TRI Authors: Nikos Arechiga, Jonathan DeCastro, Soonho Kong All Authors: Nikos Arechiga, Jonathan DeCastro, Soonho Kong, Karen Leung We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components. We explain how some of the existing ideas on runtime monitors for perception systems can be seen as a specific instance of logical scaffolds. Furthermore, we describe how logical scaffolds may be useful for improving AI programs beyond perception systems, to include general prediction systems and agent behavior models. Read More Citation: Arechiga, Nikos, Jonathan DeCastro, Soonho Kong, and Karen Leung. "Better AI through Logical Scaffolding." In FoMLaS 2019 Workshop at CAV 2019, (2019).