Regret-based multi-agent coordination with uncertain task rewards

Abstract

Many multi-agent coordination problems can be represented
as DCOPs. Motivated by task allocation in disaster response,
we extend standard DCOP models to consider uncertain task
rewards where the outcome of completing a task depends on
its current state, which is randomly drawn from unknown distributions.
The goal of solving this problem is to find a solution
for all agents that minimizes the overall worst-case loss.
This is a challenging problem for centralized algorithms because
the search space grows exponentially with the number
of agents and is nontrivial for existing algorithms for standard
DCOPs. To address this, we propose a novel decentralized algorithm
that incorporates Max-Sum with iterative constraint
generation to solve the problem by passing messages among
agents. By so doing, our approach scales well and can solve
instances of the task allocation problem with hundreds of agents
and tasks.

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