Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning
Ontology Engineering Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, 28660 Madrid, Spain
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Author to whom correspondence should be addressed.
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Current address: Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660 Madrid, Spain.
Sensors 2020, 20(18), 5198; https://doi.org/10.3390/s20185198
Received: 8 August 2020
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Revised: 5 September 2020
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Accepted: 8 September 2020
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Published: 12 September 2020
(This article belongs to the Special Issue Intelligent Sensing Techniques in Ambient Intelligence)
The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ interactions. The approach employs agent communication languages, which is a core property of these multi-agent systems, to infer theories and models about agents’ mental states that are not accessible in an open system. These mental state models can be used on their own or combined to build protocol models, allowing agents (and their developers) to predict future agents’ behavior for various tasks such as testing and debugging them or making communications more efficient, which is essential in an ambient intelligence environment. This paper’s main contribution is to explore the problem of building these agents’ mental state models not from one, but from several interaction protocols, even when the protocols could have different purposes and provide distinct ambient intelligence services.
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Keywords:
open multi-agent system; smart city; agent communication languages; agent-oriented software engineering
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MDPI and ACS Style
Serrano, E.; Bajo, J. Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning. Sensors 2020, 20, 5198. https://doi.org/10.3390/s20185198
AMA Style
Serrano E, Bajo J. Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning. Sensors. 2020; 20(18):5198. https://doi.org/10.3390/s20185198
Chicago/Turabian StyleSerrano, Emilio, and Javier Bajo. 2020. "Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning" Sensors 20, no. 18: 5198. https://doi.org/10.3390/s20185198
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