Authors:
Stefan Brüggenwirth
;
Ruben Strenzke
;
Alexander Alexander
and
Axel Schulte
Affiliation:
Bundeswehr University, Germany
Keyword(s):
Cognitive automation, Hybrid agent architecture, UAV flight guidance, Graph matching, Soar.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Autonomous Systems
;
Task Planning and Execution
Abstract:
We present an overview of our cognitive system architecture (COSA) with applications in the multi-UAV flight guidance and mission management areas. Our work is based on a modified version of the psychological Rasmussen scheme. We belief that modeling in close analogy with categories of human behavior simplifies human-machine interaction as well as the knowledge engineering process. Accordingly, our hybrid agent architecture is comprised of a low-level, reactive layer with prestored procedures and a goal-oriented, deliberative layer that enables inference and dynamic planning. The first, fully functional version of our architecture is based purely on production rules and the Soar interpreter, enhanced with syntax extensions specific to our modeling approach. We then developed our own inference machine based on graph matching which natively support extensions such as type-safety and class-inheritance and resulted in performance improvements over the original Rete algorithm of Soar. A m
ajor weakness of our current implementation still lies in its static planning functionality which is realized by a means-ends plan library. We discuss a concept that interleaves the planning process with knowledge about anticipated action outcomes, followed by an interpretation of projected future world states with respect to current goals. We illustrate this principle with a multi-UAV scenario.
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