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Arbitrage Look-ahead Agents for Market-based Optimization (ALAMO)

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: W9113M-08-C-0142
Agency Tracking Number: B063-009-0422
Amount: $999,852.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: MDA06-009
Solicitation Number: 2006.3
Solicitation Year: 2006
Award Year: 2008
Award Start Date (Proposal Award Date): 2008-04-20
Award End Date (Contract End Date): 2010-04-20
Small Business Information
625 Mount Auburn Street, Cambridge, MA, 02138
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Christopher Farnham
 Senior Software Engineer
 (617) 491-3474
Business Contact
 Jennifer Barron
Title: Director, Contracts
Phone: (617) 491-3474
Research Institution
Missile defense takes place in an unpredictable, real-time environment and thus requires an adaptive approach to optimization that dynamically allocates sensors and their supporting resources in response to changing goals and constraints. Here, we propose a system of Arbitrage Look-ahead Agents for Market-based Optimization (ALAMO) to meet the challenge of this real-time resource allocation problem. This approach applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents’ bidding strategies are based on game theory, and they are designed so that the aggregate result of the process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. We extend market-based optimization using arbitrage look-ahead agents to turn this approach into a non-myopic planning algorithm, which provides computational gains, more efficient allocations of sensor tasks, and risk management framework that hedges against uncertainty.

* Information listed above is at the time of submission. *

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