EasyCog: Affordable and Easy to Develop Cognitive Models

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-09-M-0236
Agency Tracking Number: N091-086-0321
Amount: $99,820.00
Phase: Phase I
Program: SBIR
Awards Year: 2009
Solitcitation Year: 2009
Solitcitation Topic Code: N091-086
Solitcitation Number: 2009.1
Small Business Information
Charles River Analytics Inc.
625 Mount Auburn Street, Cambridge, MA, 02138
Duns: 115243701
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Peter Weyhrauch
 Senior Scientist
 (617) 491-3474
 pweyhrauch@cra.com
Business Contact
 Ninos Hanna
Title: Senior Scientist
Phone: (617) 491-3474
Email: 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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