Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems

TL;DR

This paper introduces a decentralized auction-based algorithm for dynamic task allocation in multi-agent systems, optimizing resource use and agent satisfaction through iterative bidding and convergence analysis.

Contribution

It presents a novel greedy coalition auction algorithm that enables efficient, dynamic task allocation with convergence guarantees in multi-agent systems.

Findings

The algorithm converges after a finite number of iterations.

Numerical simulations demonstrate its effectiveness in various spatial scenarios.

The method balances task utility, agent costs, and rewards effectively.

Abstract

We propose a decentralized auction-based algorithm for the solution of dynamic task allocation problems for spatially distributed multi-agent systems. In our approach, each member of the multi-agent team is assigned to at most one task from a set of spatially distributed tasks, while several agents can be allocated to the same task. The task assignment is dynamic since it is updated at discrete time stages (iterations) to account for the current states of the agents as the latter move towards the tasks assigned to them at the previous stage. Our proposed methods can find applications in problems of resource allocation by intelligent machines such as the delivery of packages by a fleet of unmanned or semi-autonomous aerial vehicles. In our approach, the task allocation accounts for both the cost incurred by the agents for the completion of their assigned tasks (e.g., energy or fuel consumption) and the rewards earned for their completion (which may reflect, for instance, the agents' satisfaction). We propose a Greedy Coalition Auction Algorithm (GCAA) in which the agents possess bid vectors representing their best evaluations of the task utilities. The agents propose bids, deduce an allocation based on their bid vectors and update them after each iteration. The solution estimate of the proposed task allocation algorithm converges after a finite number of iterations which cannot exceed the number of agents. Finally, we use numerical simulations to illustrate the effectiveness of the proposed task allocation algorithm (in terms of performance and computation time) in several scenarios involving multiple agents and tasks distributed over a spatial 2D domain.