SOAR: an architecture for general intelligence
Artificial Intelligence
Graph grammars with negative application conditions
Fundamenta Informaticae - Special issue on graph transformations
The AGG approach: language and environment
Handbook of graph grammars and computing by graph transformation
Efficient Graph Rewriting and Its Implementation
Efficient Graph Rewriting and Its Implementation
Graph Pattern Matching in PROGRES
Selected papers from the 5th International Workshop on Graph Gramars and Their Application to Computer Science
Story Diagrams: A New Graph Rewrite Language Based on the Unified Modeling Language and Java
TAGT'98 Selected papers from the 6th International Workshop on Theory and Application of Graph Transformations
An Efficient Implementation of Graph Grammars Based on the RETE Matching Algorithm
Proceedings of the 4th International Workshop on Graph-Grammars and Their Application to Computer Science
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
Efficient Model Transformations by Combining Pattern Matching Strategies
ICMT '09 Proceedings of the 2nd International Conference on Theory and Practice of Model Transformations
Systematic editing: generating program transformations from an example
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
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Cognitive automation has proven to be an applicable approach to handle increasing complexity in automation. Although fielded prototypes have already been demonstrated, the real time performance of the underlying software framework COSA is currently a limiting factor with respect to a further increase of the application complexity. In this paper we describe a cognitive framework with increased performance for the use in cognitive systems for vehicle guidance automation tasks. It uses a combination of several existing graph transformation algorithms and techniques. We show, that for our approach, the incremental rule matching that we propose yields a performance gain over the non-incremental algorithm and a large increase over the existing generic cognitive framework COSA for a typical application.