Abstract
In this paper, we propose an architecture that uses predictions tools obtained via Bayesian learning algorithms to monitor the issues of communication, fault tolerance, and adaptation in human-agent mission. The architecture describes different level of knowledge, planning, and commands differ by their priorities. We tested the model using forest fire lookouts problem on a simulation platform (AMASE). The process uses the conjugate gradient descent algorithm to perform the Bayesian Belief Network training. The output of the training process is a well-trained BBN for agents’ prediction, estimation, and decision making during communication failure. The prediction perfection of the human and agents were compared and studied. Although results proof that human approach is prone to error but is good in terms of emergency commands execution. We suggested that the use of a well-trained prediction tool (i.e., the output BBN) could be used in monitoring mission during communication link, hardware, or software breakdown.
Original language | English |
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Title of host publication | ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence |
Editors | Ana Rocha, Luc Steels, Jaap van den Herik |
Publisher | SciTePress |
Pages | 347-354 |
Number of pages | 8 |
ISBN (Electronic) | 9789897583957 |
Publication status | Published - 2020 |
Event | 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta Duration: 22 Feb 2020 → 24 Feb 2020 |
Publication series
Name | ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence |
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Volume | 1 |
Conference
Conference | 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 |
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Country/Territory | Malta |
City | Valletta |
Period | 22/02/20 → 24/02/20 |
Bibliographical note
Publisher Copyright:Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Keywords
- Bayesian Learning
- Human-agent Team
- Mixed-initiative Planning
- Mixed-initiative Reasoning
ASJC Scopus subject areas
- Artificial Intelligence
- Software