Welfare Engineering in Practice: On the Variety of Multiagent Resource Allocation Problems

Chevaleyre, Yann; Endriss, Ulle; Estivie, Sylvia; Maudet, Nicolas

doi:10.1007/11423355_24

Cited by 15 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The insight that very different notions of social welfare may be appropriate for different applications of MARA has provided the impetus for the development of the Welfare Engineering framework [20,35], which addresses two issues:…”

Section: Welfare Engineeringmentioning

confidence: 99%

Multiagent resource allocation

Chevaleyre

Dunne

Endriss

et al. 2005

The Knowledge Engineering Review

Self Cite

192

294

View full text Add to dashboard Cite

The allocation of resources within a system of autonomous agents, that not only have preferences over alternative allocations of resources but also actively participate in computing an allocation, is an exciting area of research at the interface of Computer Science and Economics. This paper is a survey of some of the most salient issues in Multiagent Resource Allocation. In particular, we review various languages to represent the preferences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation. We also discuss pertinent issues regarding allocation procedures and present important complexity results. Our presentation of theoretical issues is complemented by a discussion of software packages for the simulation of agent-based market places. We also introduce four major application areas for Multiagent Resource Allocation, namely industrial procurement, sharing of satellite resources, manufacturing control, and grid computing.

show abstract

Section: Welfare Engineeringmentioning

confidence: 99%

Multiagent resource allocation

Chevaleyre

Dunne

Endriss

et al. 2005

The Knowledge Engineering Review

Self Cite

192

294

View full text Add to dashboard Cite

show abstract

“…MultiAgent Resource Allocation (MARA) [5] is a process of distributing a number of resources among a number of agents. Commonly, there are two types of resources, i.e.…”

Section: Multiagent Resource Allocation (Mara)mentioning

confidence: 99%

Fair Allocation through Social Norm

Wangmaeteekul¹

2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

View full text Add to dashboard Cite

In this research, fair allocation problem has been investigated in terms of efficiency and envy-freeness. The simplest allocation scenario has been chosen for this study. Ordinary Allocation Procedure and Social Norm Allocation Procedure have been proposed, implemented and tested. Three cascaded degrees of compromise values among three different-sized of agents have been set to test. The experimental results show that the Social Norm Allocation Procedure is able to reach more effectively and fairly allocated the resources among agents than the Ordinary Allocation Procedure in every agreement constant. Additionally, the smaller size of agents tends to give a better achievement when comparing with the larger size under the same agreement value.

show abstract

“…One of most important new waves of researches is based on the idea of micro-economics of agents' decision making [13,14]. Virtual money helps agents to limit the number of considered options and reduce time of computations.…”

Section: Related Workmentioning

confidence: 99%

MAGENTA technology case studies of magenta i-scheduler for road transportation

Skobelev¹,

Glaschenko²,

Grachev³

et al. 2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

View full text Add to dashboard Cite

The paper describes functionality of Magenta Multi-Agent Logistics i-Scheduler Engine presented on AAMAS 2006 conferences and gives examples of its application in business domain.The i-Scheduler Engine was designed to be scalable without risk of combinatorial explosion, in order to handle large transportation networks as a whole. The multi-agent architecture combined with semantic network allows very granular approach for every business entity of transportation network (client, order, cargo, truck, driver, etc) and balancing of their conflicting interests. The i-Scheduler considers individual constraints and, interestingly, specific preferences of customers, drivers, trucks, cargoes, etc. This results in a unique ability to combine inbound and outbound deliveries, different fleets or private networks, driving more value from finding effective backhauls and consolidations.The paper covers the history of development, architecture and current functionality of the engine and provides a set of case studies in different transportation networks, which outline the most serious challenges Magenta overcame in each case.

show abstract

Welfare Engineering in Practice: On the Variety of Multiagent Resource Allocation Problems

Cited by 15 publications

References 15 publications

Multiagent resource allocation

Multiagent resource allocation

Fair Allocation through Social Norm

MAGENTA technology case studies of magenta i-scheduler for road transportation

Contact Info

Product

Resources

About