Regret-Based Multi-Agent Coordination with Uncertain Task Rewards
This addresses task allocation in disaster response, but it is incremental as it builds on existing DCOP methods.
The paper tackles multi-agent coordination with uncertain task rewards, extending DCOP models for disaster response task allocation, and proposes a decentralized algorithm that scales to hundreds of agents and tasks.
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 standard DCOP algorithms we have. 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.