Artificial Intelligence
GCAS specializes in Artificial Intelligence techniques such as Expert Systems, Bayesian Networks and Autonomous Agents, particularly for problems involving reasoning under uncertainty. Some recent projects include:
Since our inception in 2000, we have won the following Government contracts including:
Artificial Intelligence
Since our inception in 2000, we have won the following Government contracts including:
CES is a probabilistic expert system tied to imbedded autonomous software agents in vehicle design and analysis software (CAD/CAE). The contract involved the development of a new expert tool for the early assessment of corrosion problems in vehicle.
This MDA/Navy contract is to develop new algorithms for propagation of second order uncertainty (interval data) in Bayesian Network Models as stemming from imprecise knowledge (intervals in the numbers specifying the Conditional Probability Tables), and imprecise data (intervals in the evidence provided to the model form outside sources, such as standard deviation on a sensor measurement)
This current 2-year project involves the development of a set of Decision Support Algorithms for determining the alive/dead status of a soldier, methods for performing Probabilistic Risk Assessment (PRA) analysis of a suite of sensor to be worn by the soldier, a set of sensor models used to detect sensor failure and condition the measurements according to the sensor’s reliability, and the development of a prototype Human Physiology and Sensor Simulator for modeling the combined human subject-sensors-WPSM controller system.
This NASA Ames contract involves the development of a new method of knowledgebase information retrieval for large scientific databases, in which a request for information is posed as a question, and information sources are identified that pertain to steps in the logical process of answering the question.
Knowledge Acquisition for Level II/III Fusion:
This DARPA research project involved the design and development of new software tools for eliciting knowledge from Subject-Matter Experts (SMEs) having little or no training in knowledge representation and probability theory. The work concentrated on algorithms and methodologies for acquisition and integration of mixed logical and probabilistic knowledge.
In this Army/OSD contract we are developing new prediction techniques using Artificial Intelligence methods for characterizing the failure of electronic equipment subjected to mechanical shock from blast waves created by conventional weapons.
Under National Institute of Health contract, GCAS is developing a computer software system that guides the development of Program Logic Models for program planning and evaluation. The software allows the user to develop a project plan including: resources needed for program operation (Resources/Inputs) and strategies or processes involved in implementation (Activities) link to outcomes, including process outcomes (Outputs); desired changes to the target audience as a result of the program (Outcomes); and long-term program goals (Impact). The advanced software features include Scheduling (Milestone Charts, Gantt Charts, Pert Charts, Critical Path Analysis, Critical Chain Method), Project Risk Management (Belief Networks, Influence Diagrams), Timeline Budgeting and Earned Value Management.
In this Air Force contract we are designing an interactive and continuous planning system that can handle multi-level objectives together with their deadlines and priorities. This planning system will be able to adapt to real-time information updates (for example changes in the status of resources, additional or revised mission objectives, revised priorities of objectives or target lists). In addition, the system will assist the users in specifying their assumptions, situation analysis, and external factors. Finally, the system will be designed to support centralized planning and decentralized execution.
In this Navy contract, GCAS is developing a methodology for the retrieval of corrosion data from within existing databases and the framework for an Expert System that will model system breakdown utilize existing coating data (in-service and laboratory testing).