Embedding data-driven decision strategies on software agents: The case of a multi-agent system for monitoring air-quality indexes
This paper describes the design and deployment of an agent community, which is responsible for monitoring and assessing air quality, based on measurements generated by a meteorological station. Software agents acting as mediators or decision makers deliver validated information to the appropriate destinations. We outline the procedure for creating agent ontologies, agent types, and, finally, for training agents based on historical data volumes. The C4.5 algorithm for decision tree extraction is applied on meteorological and air-pollutant measurements. The decision models extracted are related to the validation of incoming measurements and to the estimation of missing or erroneous measurements. Emphasis is given on the agent training process, which must embed these data-driven decision models on software agents in a simple and effortless way. We developed a prototype system, which demonstrates the advantages of agent-based solutions for intelligent environmental applications.
I. N. Athanasiadis, P. A. Mitkas, G. B. Laleci, Y. Kabak, Embedding data-driven decision strategies on software agents: The case of a multi-agent system for monitoring air-quality indexes, Concurrent Engineering: The Vision for the Future Generation in Research and Applications, vol. 1, pg. 23-30, 2003, Balkema Publishers.
You might also enjoy (View all publications)
- Introducing digital twins to agriculture
- Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate
- Machine learning for large-scale crop yield forecasting