Interdisciplinary Modeling of Autonomous Systems Deployed in Uncertain Dynamic Environments

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Abstract

The autonomous systems that operate in physical, random and highly dynamical environments involve a large amount of computation, which depends on the number of parameters. We tackle the problem of complexity reduction for these systems based on an interdisciplinary and qualitative approach. We define concepts like qualitative model reduction and adaptive bismulation, and use them to investigate the stochastic model checking. These concepts prove to be very useful in capturing the co-evolution between system and its environment. Potential applications in renewable energies are discussed.