mlsanityData to 7 Oct 2026

Designed to Cooperate: A Kant-Inspired Ethic of Machine-to-Machine Cooperation

arXiv 2109.13493 ↗First posted 28 Sep 2021cs.RO cs.AIcited by 0

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Abstract (arXiv)

This position paper highlights an ethic of machine-to-machine cooperation and machine pro-sociality, and argues that machines capable of autonomous sensing, decision-making and action, such as automated vehicles and urban robots, owned and used by different self-interested parties, and having their own agendas (or interests of their owners) should be designed and built to be cooperative in their behaviours, especially if they share public spaces. That is, by design, the machine should first cooperate, and then only consider alternatives if there are problems. It is argued that being cooperative is not only important for their improved functioning, especially, when they use shared resources (e.g., parking spaces, public roads, curbside space and walkways), but also as a favourable requirement analogous to how humans cooperating with other humans can be advantageous and often viewed favourably. The usefulness of such machine-to-machine cooperation are illustrated via examples including cooperative crowdsourcing, cooperative traffic routing and parking as well as futuristic scenarios involving urban robots for delivery and shopping. It is argued that just as privacy-by-design and security-by-design are important considerations, in order to yield systems that fulfil ethical requirements, cooperative-by-design should also be an imperative for autonomous systems that are separately owned but co-inhabit the same spaces and use common resources. If a machine using shared public spaces is not cooperative, as one might expect, then it is not only anti-social but not behaving ethically. It is also proposed that certification for urban robots that operate in public could be explored.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 28 Sep 2021.

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5 of 23 references.

References · 5

  1. 2020Emergent Social Learning via Multi-agent Reinforcement Learningcited by 44
  2. 2021Learning by Watchingcited by 22
  3. 2017Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networkingcited by 5
  4. 2018SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomescited by 3
  5. 2018Urban Swarms: A new approach for autonomous waste managementcited by 2

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