Sensor Synthesis for POMDPs with Reachability Objectives


  • Krishnendu Chatterjee Institute of Science and Technology Austria
  • Martin Chmelik TTTech Computertechnik AG
  • Ufuk Topcu University of Texas at Austin



POMDP, SAT, Planning


Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors. We study a setting in which the sensors are only partially defined and the goal is to synthesize “weakest” additional sensors, such that in the resulting POMDP, there is a small-memory policy for the agent that almost-surely (with probability 1) satisfies a reachability objective. We show that the problem is NP-complete, and present a symbolic algorithm by encoding the problem into SAT instances. We illustrate trade-offs between the amount of memory of the policy and the number of additional sensors on a simple example. We have implemented our approach and consider three classical POMDP examples from the literature, and show that in all the examples the number of sensors can be significantly decreased (as compared to the existing solutions in the literature) without increasing the complexity of the policies.




How to Cite

Chatterjee, K., Chmelik, M., & Topcu, U. (2018). Sensor Synthesis for POMDPs with Reachability Objectives. Proceedings of the International Conference on Automated Planning and Scheduling, 28(1), 47-55.