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Predictability of finite state machines for smart fire emergency management
2018
GEOSAFE Workshop on Robust Solutions for Fire Fighting
In this paper we present an ongoing research activity dealing with predictability properties for complex systems. We propose to apply the results derived in [FDSDB18] to predict critical situations occurring in the context of wildfires. We model fire spreading by means of a nondeterministic cellular automaton, to the aim of considering the more general case where uncertainties are present due to an imperfect knowledge of the environment. The long-term objective of this research is to optimize
dblp:conf/rsff/FioreMS18
fatcat:njpoy2s465f4dk5mxu4bdmlwym