Browsing by Author "Xu, Huan"
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Item A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems(IEEE, 2022-11-10) Borson, Joseph E.; Xu, HuanAutonomous systems promise significant improvements in many fields. These systems will be subject to legal governance requirements. The literature has largely focused on “autonomous governance” as a framework that is broadly applicable to autonomous devices regardless of the type of system (e.g., aviation or motor vehicles) at issue. While there are regulatory principles applicable to autonomous systems generally, an “autonomy-focused” approach is an inadequate lens to consider the governance of these systems. Rather, because autonomous systems are improvements of currently regulated complex systems, the regulation of autonomous elements will occur within those systems’ preexisting regulatory framework. Accordingly, the nature of future autonomous regulation will likely depend on the preexisting features of that substantive system, rather than on an optimal approach divorced from that history, an attribute known in the social science literature as path dependency. In order to characterize diverse regulated systems with an eye toward assessing future autonomous developments, we develop a framework of regulatory approaches to identify specific features of the preexisting regulatory scheme for a given system. We then analyze that approach by examining three different regulatory regimes (aviation, motor vehicles, and medical devices), across two different continents, and consider how the same type of requirement, e.g., fail-safe systems, can lead to different types of regulations depending on the differing baseline framework.Item Distributed Task Allocation Algorithms for Multi-Agent Systems with Very Low Communication(IEEE, 2022-11-23) Bapat, Akshay; Bora, Bharath Reddy; Herrmann, Jeffrey W.; Azarm, Shapour; Xu, Huan; Otte, Michael W.In this paper we explore the problem of task allocation when communication is very low, e.g., when the probability of a successful message between agents is ≪0.01 . Such situations may occur when agents choose not to communicate for reasons of stealth or when agent-to-agent communication is actively jammed by an adversary. In such cases, agents may need to divide tasks without knowing the locations of each other. Given the assumption that agents know the total number of agents in the workspace, we investigate solutions that ensure all tasks are eventually completed—even if some of the agents are destroyed. We present two task allocation algorithms that assume communication may not happen, but that benefit whenever communications are successful. (1) The Spatial Division Playbook Algorithm divides task among agents based on an area decomposition. (2) The Traveling Salesman Playbook Algorithm considers mission travel distance by leveraging Christofides’ 3/2 approximation algorithm. These algorithms have task completion runtime complexity of O(mlogm) and O(m3) , respectively, where m refers to the total number of tasks. We compare both algorithms to four state-of-the-art task allocation algorithms — ACBBA, DHBA, PIA and GA — across multiple communication levels and multiple numbers of targets, and using three different communication models. The new algorithms perform favorably, in terms of the time required to ensure all targets are visited, in the special case when communication is very low.