CIBRE Modeler Toolkit

While we often use high-fidelity architectures such as ACT-R during the course of our modeling, we had the need for a higher-performance alternative when interfacing with real-time systems.  The Cognitive Instance-Based Rule Engine (CIBRE) is a lightweight cognitive architecture developed at AdCogSys to address this need. 

CIBRE is a hybrid system, integrating low-level statistical learning capabilities with a high-level symbolic framework.  The use of this architecture gives us the ability to handle planning, inference, and hierarchically structured behavior within a framework that also supports learning directly from data, and high-speed perceptual-motor interaction, thereby spanning a breadth of interaction needs in a lightweight, scalable architecture suited to real-time applications.  Perhaps the single most compelling aspect of the architecture, however, is the ability to develop a model by demonstrating the behavior rather than through programming.

We have been continuing to use and expand CIBRE on a variety of projects, both in-house and for customers. To see CIBRE models created through demonstration rather than through programming in action, visit one of the following links:

The development of CIBRE was initially funded by an NSF phase I SBIR award to support the commercializaiton of cognitive frameworks that have a grounding in statistical learning theory.  The architecture by itself, however, is only part of the toolkit used to develop CIBRE models. The entire CIBRE Modeler Toolkit includes the following components:

• CIBRE architecture: the core cognitive architecture with facilities for planning, learning, clustering, and inferencing, plus the support software to enable debugging and visualizing results, and storing and accessing accumulated knowledge.
  
• Cognitive Agent Simulation Interaction Layer (CASIL): a middleware layer that supports interaction between arbitrary cognitive architectures and a range of simulation environments, including translation layers for vocabulary terms and knowledge, geometrical and spatial constructs and actions, that supports true portability in cognitive agents (see published paper here).
• Adaptive Mesh Refinement (AMR) tools: a software platform for conducting experiments and simulations involving a cognitive agent that allows a scientist or engineer to test a range of configurations for the agent while minimizing and optimizing the use of computational resources (see published paper here).
  

Taken together, these components make up a comprehensive suite of modeling tools that has already seen successful deployment in real-time domains.  If you would like to know how CIBRE might help you address your modeling needs, please contact us for more information.