pydcop.distribution.adhoc

Ad-hoc distribution

This is a heuristic design for [some?] IJCAI 2016 paper. The distribution respects the agents’ capacities, and thus requires an estimation function for the computation footprint, and honors so-called ‘hints’.

distribute(computation_graph: ComputationGraph, agentsdef: Iterable[AgentDef], hints: DistributionHints | None = None, computation_memory=None, communication_load=None, timeout=None)

Generate a distribution for the DCOP. This method uses a simple heuristic for distribution, with no guarantee of optimality.

Even if a feasible distribution exists, this method is not guaranteed to find it.

When using a DCOP that represents an SECP, given the correct DistributionHint the same distribution should be generated that with the adhoc SECP distribution method.

Notes

The timeout argument is accepted to honor the protocol in pydcop distribute but ignored.