Transfer Entropy in MDPs with Temporal Logic Specifications
For autonomous systems requiring high-level mission specifications under communication constraints, this work provides a principled control synthesis method.
The paper addresses control synthesis in MDPs with temporal logic specifications under communication constraints, minimizing a weighted sum of transfer entropy and failure probability. The method is demonstrated on a Mars rover navigation scenario.
Emerging applications in autonomy require control techniques that take into account uncertain environments, communication and sensing constraints, while satisfying highlevel mission specifications. Motivated by this need, we consider a class of Markov decision processes (MDPs), along with a transfer entropy cost function. In this context, we study highlevel mission specifications as co-safe linear temporal logic (LTL) formulae. We provide a method to synthesize a policy that minimizes the weighted sum of the transfer entropy and the probability of failure to satisfy the specification. We derive a set of coupled non-linear equations that an optimal policy must satisfy. We then use a modified Arimoto-Blahut algorithm to solve the non-linear equations. Finally, we demonstrated the proposed method on a navigation and path planning scenario of a Mars rover.