I Think Therefore You Are

Author:

Esterle Lukas1ORCID,Brown John N. A.2

Affiliation:

1. Aarhus University, Finlandsgade, Aarhus, Denmark

2. Evolv Technologies, San Francisco, CA, USA

Abstract

Cyber-physical systems operate in our real world, constantly interacting with the environment and collaborating with other systems. The increasing number of devices will make it infeasible to control each one individually. It will also be infeasible to prepare each of them for every imaginable rapidly unfolding situation. Therefore, we must increase the autonomy of future Cyber-physical Systems. Making these systems self-aware allows them to reason about their own capabilities and their immediate environment. In this article, we extend the idea of the self-awareness of individual systems toward networked self-awareness . This gives systems the ability to reason about how they are being affected by the actions and interactions of others within their perceived environment, as well as in the extended environment that is beyond their direct perception. We propose that different levels of networked self-awareness can develop over time in systems as they do in humans. Furthermore, we propose that this could have the same benefits for networks of systems that it has had for communities of humans, increasing performance and adaptability.

Funder

SOLOMON project

European Union H2020 Programme

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Conclusion;Computational Modelling of Robot Personhood and Relationality;2023-10-17

2. Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges;2023 Design, Automation & Test in Europe Conference & Exhibition (DATE);2023-04

3. A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems;ACM Transactions on Autonomous and Adaptive Systems;2022-06-30

4. Deep learning in multiagent systems;Deep Learning for Robot Perception and Cognition;2022

5. Self-improving system integration: Mastering continuous change;Future Generation Computer Systems;2021-04

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