In this work, we investigate the concept of "restraining bolt'", envisioned in Science Fiction. Specifically, we introduce a novel problem in AI. We have two distinct sets of features extracted from the world, one by the agent and one by the authority imposing restraining specifications (the "restraining bolt"). The two sets are apparently unrelated since of interest to independent parties, however, they both account for (aspects of) the same world. We consider the case in which the agent is a reinforcement learning agent on the first set of features, while the restraining bolt is specified logically using linear time logic on finite traces LTLf/LDLf over the second set of features. We show formally, and illustrate with examples, that, under general circumstances, the agent can learn while shaping its goals to suitably conform (as much as possible) to the restraining bolt specifications.
2019, Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, Pages 128-136 (volume: 29)
Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf Restraining Specifications (04b Atto di convegno in volume)
DE GIACOMO Giuseppe, IOCCHI Luca, FAVORITO MARCO, PATRIZI FABIO