Extending HLSR to Support Scaling Up to Complex Models for Training, Simulation, and Robotics

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-09-M-0235
Agency Tracking Number: N091-086-0451
Amount: $69,992.00
Phase: Phase I
Program: SBIR
Awards Year: 2009
Solicitation Year: 2009
Solicitation Topic Code: N091-086
Solicitation Number: 2009.1
Small Business Information
Soar Technology, Inc.
3600 Green Court, Suite 600, Ann Arbor, MI, 48105
DUNS: 009485124
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Randolph Jones
 Senior Scientist
 (207) 649-1895
 rjones@soartech.com
Business Contact
 Katherine Harding
Title: Senior Scientist
Phone: (734) 327-8000
Email: kate.harding@soartech.com
Research Institution
N/A
Abstract
A long-term, cost-effective approach to addressing increased mission complexity and cost is to increase automation in operations and training. Although a variety of automation exists, the next significant advance will be to automate decision-making processes that currently rely on human experts. Such experts take years to train and are only available for a limited time, whereas intelligent software systems can be copied without limit, and they do not age or retire. Although now technically feasible to build intelligent decision-making systems, it remains expensive and difficult to engineer them, as well as to carry out the necessary supporting research into psychological modeling. However, High-level languages for software engineering have proven extremely effective at reducing costs for the development of complex software. Soar Technology, Inc. proposes to bring similar effectiveness to decision-making system engineering, reducing the cost of development and maintenance by 4-5 times. We will accomplish this by extending an existing HLSR language and compiler with knowledge patterns for building increasingly complex and human-like models. While this effort will primarily payoff for engineered systems, we argue that it also will improve the cost effectiveness and scientific consistency of cognitive modeling research.

* information listed above is at the time of submission.

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