Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
A model and a language for the fuzzy representation and handling of time
Fuzzy Sets and Systems
Developing multi-agent systems with a FIPA-compliant agent framework
Software—Practice & Experience
Fuzzy constraint networks for signal pattern recognition
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
A fuzzy constraint satisfaction approach for signal abstraction
International Journal of Approximate Reasoning
Addressing the flaws of current critical alarms: a fuzzy constraint satisfaction approach
Artificial Intelligence in Medicine
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In this work we present TRACE, a tool for projecting the description of a signal pattern, obtained directly from an expert in the application domain, onto a computational model, and automatically identifying the pattern over a recording of the temporal evolution of a physical system. Knowledge acquisition and the visualization of results from detection are based on visual metaphors that enable experts to simply and intuitively describe patterns and identify them over a signal recording. TRACE has been used in the domains of mobile robotics and patient supervision in the intensive care units, with highly satisfactory results. The multivariable fuzzy temporal profile model (MFTP), which supports the tool, describes a signal pattern as a network of fuzzy constraints between a set of points from the evolution of the system which are especially relevant for experts. The constraint network formalism permits a single pattern to be described as a set of increments, temporal durations and slopes between the relevant points of the system's evolution. Thanks to the use of fuzzy logic, the vagueness and uncertainty that are characteristic of human knowledge can be captured by means of the representation of imprecise values for the aforementioned parameters.