Machine learning for time interval petri nets

Authors:
Vadim Bulitko;David C. Wilkins
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Center for the Study of Language and Information, Stanford University, Stanford, CA
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 1
Cited 2

Qualitative simulation of temporal concurrent processes using Time Interval Petri Nets

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Qualitative simulation of temporal concurrent processes using Time Interval Petri Nets

Artificial Intelligence
Exploiting petri-net structure for activity classification and user instruction within an industrial setting

ICMI '11 Proceedings of the 13th international conference on multimodal interfaces

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Abstract

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.