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      Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks

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          Abstract

          The behavior of self-driving cars must be compatible with an enormous set of conflicting and ambiguous objectives, from law, from ethics, from the local culture, and so on. This paper describes a new way to conveniently define the desired behavior for autonomous agents, which we use on the self-driving cars developed at nuTonomy. We define a "rulebook" as a pre-ordered set of "rules", each akin to a violation metric on the possible outcomes ("realizations"). The rules are partially ordered by priority. The semantics of a rulebook imposes a pre-order on the set of realizations. We study the compositional properties of the rulebooks, and we derive which operations we can allow on the rulebooks to preserve previously-introduced constraints. While we demonstrate the application of these techniques in the self-driving domain, the methods are domain-independent.

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          Most cited references14

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          A Fully Automated Framework for Control of Linear Systems from Temporal Logic Specifications

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            Provably safe and robust learning-based model predictive control

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              Linear Time Logic Control of Discrete-Time Linear Systems

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                Author and article information

                Journal
                25 February 2019
                Article
                1902.09355
                70035d94-7eef-4d74-964b-56c9b0ef5e85

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.AI

                Artificial intelligence
                Artificial intelligence

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