Web Services Essentials
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
Artificial Intelligence Review
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
Coalition formation mechanism in multi-agent systems based on genetic algorithms
Applied Soft Computing
Hybrid multi-agent architecture as a real-time problem-solving model
Expert Systems with Applications: An International Journal
Enabling run-time composition and support for heterogeneous pervasive multi-agent systems
Journal of Systems and Software
Intelligent environment for monitoring Alzheimer patients, agent technology for health care
Decision Support Systems
Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEEE Transactions on Neural Networks
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Dynamic environments represent a quite complex domain, where the information available changes continuously. In this paper, a contingency response system for dynamic environments called MACSDE is presented. The explained system allows the introduction of information, the monitoring of the process and the generation of predictions. The system makes use of a Case-Based Reasoning system which generates predictions using previously gathered information. It employs a distributed multi-agent architecture so that the main components of the system can be accessed remotely. Therefore, all functionalities can communicate in a distributed way, even from mobile devices. The core of the system is a group of deliberative agents acting as controllers and administrators for all functionalities. The system explained includes a novel network for data classification and retrieval. Such network works as a summarization algorithm for the results of an ensemble of Self-Organizing Maps. The presented system has been tested with data related with oil spills and forest fire, obtaining quite hopeful results.