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Authors: Sagir Muhammad Yusuf and Chris Baber

Affiliation: University of Birmingham, B15 2TT, U.K.

Keyword(s): Multi-agent Learning, DCOP, Bayesian Learning, Bayesian Inference.

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 multi-agent 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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Yusuf, S. and Baber, C. (2020). Handling Uncertainties in Distributed Constraint Optimization Problems using Bayesian Inferential Reasoning. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 881-888. DOI: 10.5220/0009157108810888

@conference{icaart20,
author={Sagir Muhammad Yusuf. and Chris Baber.},
title={Handling Uncertainties in Distributed Constraint Optimization Problems using Bayesian Inferential Reasoning},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={881-888},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009157108810888},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Handling Uncertainties in Distributed Constraint Optimization Problems using Bayesian Inferential Reasoning
SN - 978-989-758-395-7
IS - 2184-433X
AU - Yusuf, S.
AU - Baber, C.
PY - 2020
SP - 881
EP - 888
DO - 10.5220/0009157108810888
PB - SciTePress

- Science and Technology Publications, Lda.
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