3 research outputs found
DisCSPs with Privacy Recast as Planning Problems for Utility-based Agents
Privacy has traditionally been a major motivation for decentralized problem
solving. However, even though several metrics have been proposed to quantify
it, none of them is easily integrated with common solvers. Constraint
programming is a fundamental paradigm used to approach various families of
problems. We introduce Utilitarian Distributed Constraint Satisfaction Problems
(UDisCSP) where the utility of each state is estimated as the difference
between the the expected rewards for agreements on assignments for shared
variables, and the expected cost of privacy loss. Therefore, a traditional
DisCSP with privacy requirements is viewed as a planning problem. The actions
available to agents are: communication and local inference. Common
decentralized solvers are evaluated here from the point of view of their
interpretation as greedy planners. Further, we investigate some simple
extensions where these solvers start taking into account the utility function.
In these extensions we assume that the planning problem is further restricting
the set of communication actions to only the communication primitives present
in the corresponding solver protocols. The solvers obtained for the new type of
problems propose the action (communication/inference) to be performed in each
situation, defining thereby the policy
Utilitarian Distributed Constraint Optimization Problems
Privacy has been a major motivation for distributed problem optimization.
However, even though several methods have been proposed to evaluate it, none of
them is widely used. The Distributed Constraint Optimization Problem (DCOP) is
a fundamental model used to approach various families of distributed problems.
As privacy loss does not occur when a solution is accepted, but when it is
proposed, privacy requirements cannot be interpreted as a criteria of the
objective function of the DCOP. Here we approach the problem by letting both
the optimized costs found in DCOPs and the privacy requirements guide the
agents' exploration of the search space. We introduce Utilitarian Distributed
Constraint Optimization Problem (UDCOP) where the costs and the privacy
requirements are used as parameters to a heuristic modifying the search
process. Common stochastic algorithms for decentralized constraint optimization
problems are evaluated here according to how well they preserve privacy.
Further, we propose some extensions where these solvers modify their search
process to take into account their privacy requirements, succeeding in
significantly reducing their privacy loss without significant degradation of
the solution quality
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