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Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
C. H. Dagli
C. H. Dagli
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ASME Press
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Technological environments are growing in complexity and their governance is difficult. To improve the timeliness and accuracy of nonhuman decision-making in a system environment we are faced with an array of crucial problems, including how to handle large amounts of incoming and uncertain information from disparate sources coded as events of different type. Artificial Intelligence has developed theories to manage events and actions (McCarthy, Kowalski) and we present an event calculus formalism featuring events and action integrated with temporal logic. We have designed and realized an algorithm that permits to a software system to be able to forecast future actions depending on events happening in a process area. The conditional relationships are calculated starting from frequency of conditional events and the resulting possible states are calculated. Each conditional event can trigger event-action modules.

Sensor Networks
Modeling a Sensors Net System
Events Calculus
Conditional Events Probabilistic Knowledge
Conditional Events Probabilistic Knowledge Construction
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