Distributed Constraint Problems for Utilitarian Agents with Privacy
  Concerns, Recast as POMDPs
  Privacy has traditionally been a major motivation for distributed problem
solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as
Distributed Constraint Optimization Problem (DCOP) are fundamental models used
to solve various families of distributed problems. Even though several
approaches have been proposed to quantify and preserve privacy in such
problems, none of them is exempt from limitations. Here we approach the problem
by assuming that computation is performed among utilitarian agents. We
introduce a utilitarian approach where the utility of each state is estimated
as the difference between the reward for reaching an agreement on assignments
of shared variables and the cost of privacy loss. We investigate extensions to
solvers where agents integrate the utility function to guide their search and
decide which action to perform, defining thereby their policy. We show that
these extended solvers succeed in significantly reducing privacy loss without
significant degradation of the solution quality.
