S&AS: FND: Norm Processing for Autonomous Social Systems

Tufts University, Medford MA

Investigators

Abstract

As intelligent systems continue to become more autonomous, it is important to imbue such systems with the types of social and moral norms that are deeply ingrained in human cognition and behavior. Failing to abide by these norms typically causes social reactions from humans, from blame and reprimands, in simple cases, all the way to full-fledged legal consequences. Given the fundamental social expectations humans have of each other, it is very likely that they will extend and apply those to artificial agents as well. Hence, intelligent autonomous systems will need to be endowed with computational mechanisms that ensure their ethical behavior. This project will develop explicit norm representations and planning algorithms for norm-conforming behavior that will address shortcomings of previous approaches, enabling explicit, temporally complex norm representations in stochastic worlds that allow for systematic handling of norm conflicts in ways that maximize the norms an agent can obey at any moment in time. In particular, explicit norm specifications will allow for generalization across unobserved states. In addition, it is important for systems interacting with humans to make decisions that are transparent. To that end, the approach will enable the generation of justifications in cases where an autonomous system is forced to violate some norms because not all can be satisfied simultaneously. The approach and algorithms developed will be demonstrated on a fully autonomous cleaning robot with predefined norms in different scenarios, where conflicting norms are applicable and the robot must determine which norms to follow and which to suspend.