Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms

Sriram Sankaranarayanan (1) , Yi Chou (1) , Eric Goubault (2) , Sylvie Putot (2)

1 University of Colorado [Boulder]
2 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]

In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems. This problem arises in a variety of applications in areas ranging from biology to robotics. Our approach allows us to rigorously represent the probability distribution of state variables over time, and provide guaranteed bounds on the expectations, moments and probabilities of tail events involving the state variables. First, we recall ideas from interval arithmetic, and use them to rigorously represent the state variables at time t as a function of the initial state variables and noise symbols that model the random exogenous inputs encountered before time t. Next, we show how concentration of measure inequalities can be employed to prove rigorous bounds on the tail probabilities of these state variables. We demonstrate interesting applications that demonstrate how our approach can be useful in some situations to establish mathematically guaranteed bounds that are of a different nature from those obtained through simulations with pseudo-random numbers.

Domaines: Intelligence artificielle [cs.AI]; Logique en informatique [cs.LO]

Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot. Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. 2020 Conference on Neural Information Processing Systems (NeurIPS 2020), Dec 2020, Online, France. ⟨hal-03237392⟩