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ML-TIPN: An Algorithm for Automated Acquisition of Domain Models based on Time Interval Petri Nets
Vadim Bulitko and David C.Wilkins

Extended Petri Nets have been applied to artificial intelligence reasoning processes, in areas such as planning, uncertainty reasoning, knowledge-based intelligent systems, and qualitative simulation. Creating Petri Net domain models faces the same challenges that confront all knowledge-intensive AI performance systems: model specification, knowledge acquisition, and refinement. Thus, a fundamental question to investigate is the degree to which automation can be used. This paper formulates the learning task and presents the first machine learning method for Time Interval Petri Net (TIPN) domain models. In a preliminary evaluation within a damage control domain, the method learned a nearly perfect model of fire spread augmented with temporal and spatial data.

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