eCite Digital Repository

Dynamic source weight computation for truth inference over data streams

Citation

Yang, Y and Bai, Q and Liu, Q, Dynamic source weight computation for truth inference over data streams, AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 13-17 May 2019, Montreal, Canada, pp. 277-285. ISBN 978-1-4503-6309-9 (2019) [Refereed Conference Paper]


Preview
PDF
Restricted - Request a copy
2Mb
  

Copyright Statement

Copyright 2019 2019 International Foundation for Autonomous Agents and Multiagent Systems

DOI: doi:10.5555/3306127.3331704

Abstract

Truth inference, a method that resolves conflicts among multi-agent data, has been widely studied in the field of AI. Most existing truth inference methods use iterative approaches to achieve high accuracy, but are inefficient to infer object truths over data streams. The methods developed for streaming data can achieve high efficiency but suffer from low accuracy. In this paper, we propose a novel truth inference method, Dynamic Source Weight Computation truth inference (DSWC), that can work with a wide range of iterative-based truth inference methods to dynamically compute source weights over data streams. Specifically, we use Taylor expansion to analyze the unit error of object truths inferred by source weights computed at a previous timestamp. If the source weight at present is predicted to be able to limit the error under a threshold, we use the source weights computed previously to approximate object truths at present to avoid the expensive source weight computation step. Compared with the existing work, the proposed method is more effective in predicting source weights and can be applied to a wider range of applications. Experimental results based on four real-world datasets demonstrate that DSWC is both accurate and efficient for truth inference over data streams.

Item Details

Item Type:Refereed Conference Paper
Keywords:truth discovery, knowledge discovery, trust
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138231
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-05-27
Downloads:0

Repository Staff Only: item control page