Robust Specification Mining from Demonstrations

We consider the problem of inferring temporal specifications from demonstrations by an agent interacting with an uncertain, stochastic environment. Such specifications are useful for correct-by-construction control of autonomous systems operating in uncertain environments. Some demonstrations may have errors, and the specification inference method must be robust to them. We provide a novel formulation of the problem as a maximum a posteriori (MAP) probability inference problem, and give an efficient approach to solve this problem, demonstrated by case studies inspired by robotics.

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