EasyCog: Affordable and Easy to Develop Cognitive Models

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,945.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
N00014-09-M-0236
Agency Tracking Number:
N091-086-0321
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
CHARLES RIVER ANALYTICS, INC.
625 Mount Auburn Street, Cambridge, MA, 02138
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
115243701
Principal Investigator:
Peter Weyhrauch
Senior Scientist
(617) 491-3474
pweyhrauch@cra.com
Business Contact:
Ninos Hanna
Senior Scientist
(617) 491-3474
nhanna@cra.com
Research Institution:
n/a
Abstract
Applications that incorporate Human Behavior Models (HBMs) have the potential to improve the training, decision-making, and even the operational effectiveness of US forces. This software can mimic human decision-making, reasoning, learning, social and cultural biases, as well as perceptual, cognitive, and motor limitations in support of many applications, including analysis, simulation-based acquisition, training, and mission rehearsal. Unfortunately, the development of such complex software systems is currently costly, time consuming, and error-prone due to a lack of appropriate high-level languages and corresponding development tools. To improve the affordability, efficiency and robustness of the HBM-development process, we propose to design and demonstrate the feasibility of EasyCog, a high-level cognitive language, compiler, and development environment that is designed to improve the HBM-development process across multiple cognitive architectures. EasyCog combines cognitive, behavioral, social, affect, and cultural models with high-level programming language features to improve the breadth and scalability of HBMs. Visual Editors and Debuggers work at the level of the high-level language to improve the efficiency of model development. Finally, EasyCog can generate configurable source code for a variety of existing cognitive architectures, meaning runtime users can reuse models in a variety of domains based on a single high-level model.

* information listed above is at the time of submission.

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