---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Krishnendu
      foaf_name: Chatterjee, Krishnendu
      foaf_surname: Chatterjee
      foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-4561-241X
  - foaf_Person:
      foaf_givenName: Martin
      foaf_name: Chemlík, Martin
      foaf_surname: Chemlík
  - foaf_Person:
      foaf_givenName: Ufuk
      foaf_name: Topcu, Ufuk
      foaf_surname: Topcu
  bibo_doi: 10.1609/icaps.v28i1.13875
  bibo_volume: 2018
  dct_date: 2018^xs_gYear
  dct_identifier:
  - UT:000492986200006
  dct_language: eng
  dct_publisher: AAAI Press@
  dct_title: Sensor synthesis for POMDPs with reachability objectives@
...