We propose an episodic memory-based approach to the problem of pattern capture and recognition.
We show how a generic episodic memory module can be enhanced with an incremental retrieval algorithm that can deal with the kind of data available for this application.
We evaluate this approach on a goal schema recognition task on a complex and noisy dataset.
The memory module was able to achieve the same level of performance as statistical approaches and doing so in a scalable manner.

@InProceedings{tecuci-em-fs-06,
 author    = 	 {Dan Tecuci and Bruce Porter},
 title     = 	 {{U}sing an {E}pisodic {M}emory {M}odule for 
                  {P}attern {C}apture and {R}ecognition},
 booktitle = 	 {Capturing and Using Patterns for Evidence 
                  Detection: Papers from the 2006 Fall Symposium.},
 publisher =   {AAAI Press},
 year      = 	 {2006},
 editor    = 	 {Ken Murray and Ian Harrison},
 note      = 	 {Technical Report FS-06-02},
 address   = 	 {Menlo Park, CA},
}