Machine Reasoning for Effects-Based Operations: A Generic Architecture for Multi-Domain Workarounds Reasoning

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$741,224.00
Award Year:
2003
Program:
SBIR
Phase:
Phase II
Contract:
F30602-03-C-0045
Agency Tracking Number:
021IF-0918
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
ALPHATECH, INC.
6 New England Executive Park, Burlington, MA, 01803
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
094841665
Principal Investigator:
Daniel Hunter
Lead Engineer
(781) 273-3388
dhunter@alphatech.com
Business Contact:
Andrew Mullin
General Counsel
(781) 273-3388
andy.mullin@alphatech.com
Research Institution:
n/a
Abstract
Effects-based operations must determine how the enemy might respond to air strikes. Current approaches to predicting enemy response to target damage suffer from serious limitations: they typically do not consider how the enemy might repair or modify thestructure of a target system, they typically reason only about a single type of target system, they cannot adequately represent action duration, concurrent actions, and uncertainty, and their models are difficult for analysts to construct and maintain.In Phase I, we developed a machine reasoning architecture and associated algorithms that address these problems, and implemented a prototype to validate the approach. Our design integrates emerging knowledge acquisition technology with novel extensions tostate-of-the-art resource allocation and Hierarchical Task Network (HTN) planning technology, to provide a generic, configurable framework for computing near-optimal workarounds that reconstitute or otherwise modify network systems to accomplishobjectives.In Phase II, we will fully implement our design and demonstrate its application to computing structural workarounds in multiple network systems, including ground transportation, POL, and electric power. The resulting system will automatically computeworkaround options and predict enemy allocations of resources to achieve time-based, capacitated objectives. It will incorporate extensions to handle uncertainty regarding initial conditions and adversary capabilities, and it will provide knowledgeacquisition technology enabling analysts to readily build and maintain models of these network systems and associated workaround procedures.

* information listed above is at the time of submission.

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