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.
Citation
@inproceedings{9472783, title = {The Case for AI Based Web3 Reputation Systems}, author = {Keizer, Navin V. and Yang, Fan and Psaras, Ioannis and Pavlou, George}, year = 2021, booktitle = {2021 IFIP Networking Conference (IFIP Networking)}, pages = {1--2}, doi = {10.23919/IFIPNetworking52078.2021.9472783}, keywords = {Reinforcement learning;Blockchain;Resource management;Artificial intelligence;Reputation System;Deep Reinforcement Learning;Blockchain;Web3;Resource Sharing Services}}