Learn More
This paper addresses the problem of deciding effectively whether to interrupt a teammate who may have information that is valuable for solving a collaborative scheduling problem. Two characteristics of multi-agent scheduling complicate the determination of the value of the teammate's information, and hence whether it exceeds the costs of an interruption.(More)
Machine learning, statistics and knowledge engineering provide a broad variety of supervised learning algorithms for classification. In this paper we introduce the Automated Model Selection Framework (AMSF) which presents automatic and semi-automatic methods to select classifiers. To achieve this we split up the selection process into three distinct phases.(More)
The need for tools to aid the selection of the CI models that lie at the heart of many AI systems has never been greater, due to the mainstreaming of data mining and other AI applications. LEONARDO -our contribution to this process- is a recommender system that selects and ranks applicable CI models for a given problem based on the peculiarities of the(More)
The peculiarities of the aircraft monitoring and maintenance domain are described and shortcomings of the current monitoring methodology are revealed. It is also shown why a new approach using computational intelligence models, as a replacement for the current BITE models, is paramount. In section 2 a brief review of developments in computational(More)
New approaches to managing flood events are increasingly of more relevance due to recent widespread floods and the presumed changes in the climate. These approaches fall under the integrated flood management (IFM) banner and focus not only on flood prevention, but on flood resilience. This paper introduces an application (FLORETO) for IFM that utilizes the(More)
  • 1