This extended abstract introduces the initial steps taken to develop a system for Rapid Internal Simulation of Knowledge (RISK). RISK aims to enable more transparency in artificial intelligence systems, especially those created by deep learning networks by allowing real-time simulation of what the system knows. By looking at hypothetical situations based on these simulations a system may make more informed decisions, and produce them for non-expert observers to understand the reasoning behind a given action.
@article{arxiv.2208.07306,
title = {Introducing RISK},
author = {Christopher D. Wallbridge and Qiyuan Zhang},
journal= {arXiv preprint arXiv:2208.07306},
year = {2022}
}
Comments
in TRAITS Workshop Proceedings (arXiv:2206.08270) held in conjunction with Companion of the 2022 ACM/IEEE International Conference on Human-Robot Interaction, March 2022, Pages Pages 1284-1286