Distributed constraint reasoning

Distributed constraint optimization (DCOP or DisCOP) is the distributed analogue to constraint optimization. A DCOP is a problem in which a group of agents must distributedly choose values for a set of variables such that the cost of a set of constraints over the variables is either minimized or maximized.

Distributed Constraint Satisfaction is a framework for describing a problem in terms of constraints that are known and enforced by distinct participants (agents). The constraints are described on some variables with predefined domains, and have to be assigned to the same values by the different agents.

Problems defined with this framework can be solved by any of the algorithms that are proposed for it.

The framework was used under different names in the 1980s. The first known usage with the current name is in 1990.

A DCOP can be defined as a tuple ${\displaystyle \langle A,V,{\mathfrak {D}},f,\alpha ,\eta \rangle }$, where:

The objective of a DCOP is to have each agent assign values to its associated variables in order to either minimize or maximize ${\displaystyle \eta (f)}$ for a given assignment of the variables.

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