EasyCog: Affordable and Easy to Develop Cognitive Models

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$1,491,673.00
Award Year:
2010
Program:
SBIR
Phase:
Phase II
Contract:
N00014-10-C-0281
Award Id:
92589
Agency Tracking Number:
N091-086-0321
Solicitation Year:
2009
Solicitation Topic Code:
N091-086
Solicitation Number:
2009.1
Small Business Information
625 Mount Auburn Street, Cambridge, MA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
115243701
Principal Investigator:
PeterWeyhrauch
Senior Scientist
(617) 491-3474
pweyhrauch@cra.com
Business Contact:
NinosHanna
Contract Specialist
(617) 491-3474
nhanna@cra.com
Research Institute:
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, development environment, and compiler 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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