Handling uncertainties in distributed constraint optimization problems using Bayesian inferential reasoning

Sagir Muhammad Yusuf, Chris Baber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose the use of Bayesian inference and learning to solve DCOP in dynamic and uncertain environments. We categorize the agents Bayesian learning process into local learning or centralized learning. That is, the agents learn individually or collectively to make optimal predictions and share learning data. The agents' mission data is subjected to gradient descent or expectation-maximization algorithms for training purposes. The outcome of the training process is the learned network used by the agents for making predictions, estimations, and conclusions to reduce communication load. Surprisingly, results indicate that the algorithms are capable of producing accurate predictions using uncertain data. Simulation experiment result of a multiagent mission for wildfire monitoring suggest robust performance by the learning algorithms using uncertain data. We argue that Bayesian learning could reduce the communication load and improve DCOP algorithms scalability.

Original languageEnglish
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages881-888
Number of pages8
ISBN (Electronic)9789897583957
Publication statusPublished - 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: 22 Feb 202024 Feb 2020

Publication series

NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
Country/TerritoryMalta
CityValletta
Period22/02/2024/02/20

Bibliographical note

Funding Information:
The authors wish to express their gratitude and appreciation for any comments that help in making this paper a great one. The authors wish to also express their appreciation to Petroleum Technology Trust Fund (PTDF) of Nigeria for the sponsorship of this research.

Publisher Copyright:
Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Keywords

  • Bayesian Inference
  • Bayesian Learning
  • DCOP
  • Multi-agent Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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