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The Case for AI Based Web3 Reputation Systems

Keizer, NV; Yang, F; Psaras, I; Pavlou, G; (2021) The Case for AI Based Web3 Reputation Systems. In: Proceedings of the 2021 IFIP Networking Conference (IFIP Networking). IEEE: Espoo and Helsinki, Finland. Green open access

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Abstract

Initiatives such as blockchains and decentralized storage networks are pushing for a decentralized Web3 to replace the current architecture. At the core of Web3 are network resource sharing services, which allow anyone to sell spare network capacity in return for rewards. These services require a way to establish trust, as parties are potentially malicious. This can be achieved by reputation systems. In this paper we make the case for using deep reinforcement learning in Web3 reputation calculation. More specifically, we propose a model which allows for decentralized calculation of scores with high personalization for the user.

Type: Proceedings paper
Title: The Case for AI Based Web3 Reputation Systems
Event: 2021 IFIP Networking Conference (IFIP Networking)
ISBN-13: 9783903176393
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/IFIPNetworking52078.2021.9472783
Publisher version: http://dx.doi.org/10.23919/IFIPNetworking52078.202...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Reinforcement learning, Blockchain, Resource management, Artificial intelligence
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10135323
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