When do agents outperform centralized algorithms?

Abstract

Multi-agent systems (MAS) literature often assumes decentralized MAS to be especially suited for dynamic and large scale problems. In operational research, however, the prevailing paradigm is the use of centralized algorithms. Present paper empirically evaluates whether a multi-agent system can outperform a centralized algorithm in dynamic and large scale logistics problems. This evaluation is novel in three aspects: 1) to ensure fairness both implementations are subject to the same constraints with respect to hardware resources and software limitations, 2) the implementations are systematically evaluated with varying problem properties, and 3) all code is open source, facilitating reproduction and extension of the experiments. Existing work lacks a systematic evaluation of centralized versus decentralized paradigms due to the absence of a real-time logistics simulator with support for both paradigms and a dataset of problem instances with varying properties. We extended an existing logistics simulator to be able to perform real-time experiments and we use a recent dataset of dynamic pickup-and-delivery problem with time windows instances with varying levels of dynamism, urgency, and scale. The OptaPlanner constraint satisfaction solver is used in a centralized way to compute a global schedule and used as part of a decentralized MAS based on the dynamic contract-net protocol (DynCNET) algorithm. The experiments show that the DynCNET MAS finds solutions with a relatively lower operating cost when a problem has all following three properties: medium to high dynamism, high urgency, and medium to large scale. In these circumstances, the centralized algorithm finds solutions with an average cost of 112.3% of the solutions found by the MAS. However, averaged over all scenario types, the average cost of the centralized algorithm is 94.2%. The results indicate that the MAS performs best on very urgent problems that are medium to large scale.

Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints

“…Furthermore, we see that the complexity of the local optimization problem (7) in SOE is essentially dictated by the matrix inversion in (7). Applying the inversion naively, the complexity is O(N 3 ), the dimensionality of the estimated vector.…”

“…It is obvious to see that by cooperation, swarms can speed up their mission task significantly. The gain in speed can either be due to performing parallel measurements or due to splitting the computational load among the agents [7]. Additionally, cooperation can open a way for more autonomy in such tasks and as a consequence reduce the mission costs [3].…”

Intelligent Energy Management for Off-Grid Renewable Hybrid System Using Multi-Agent Approach

“…To know how to operate and find its specific network, each local agent will be responsible for defining these functions. Moreover, none of the agents has reached the knowledge of the whole domain [56]. A summary of MAS architectures is given by the Table 1.…”

A Route Planner Using a Delegate Multi-Agent System for a Modular Manufacturing Line: Proof of Concept

“…The application we have hereinbefore described, requires a continuous, decentralized route planner, suitable for being used in a dynamic environment. Decentralized solutions, such as MAS, can outperform centralized planners, if the application has the following properties: high urgency, medium to high dynamism, and medium to large scale [11].…”