Authors:
Jinhong K. Guo
1
;
Alexander Karlovitz
2
;
Patrick Jaillet
3
and
Martin O. Hofmann
1
Affiliations:
1
Lockheed Martin Advanced Technology Laboratories, 3 Executive Campus, Suite 600, Cherry Hill, NJ 08002 and U.S.A.
;
2
Department of Mathematics, Rutgers University, Piscataway, NJ 08854 and U.S.A.
;
3
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139 and U.S.A.
Keyword(s):
Resource Allocation, Resource Optimization, Auction-based Approach, Decentralized Resource Allocation.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Mobile Agents
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
Optimizing decision quality in large scale, distributed, resource allocation problems requires selecting the appropriate decision network architecture. Such resource allocation problems occur in distributed sensor networks, military air campaign planning, logistics networks, energy grids, etc. Optimal solutions require that demand, resource status, and allocation decisions are shared via messaging between geographically distributed, independent decision nodes. Jamming of wireless links, cyber attacks against the network, or infrastructure damage from natural disasters interfere with messaging and, thus, the quality of the allocation decisions. Our contribution described in the paper is a decentralized resource allocation architecture and algorithm that is robust to significant message loss and to uncertain demand arrival, and provides fine-grained, many-to-many combinatorial task allocation. Most importantly, it enables a conscious choice of the best level of decentralization under t
he expected degree of communications denial and quantifies the benefits of approximating status of peer nodes using proxy agents during temporary communications loss.
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