Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11790/340
Title: Towards a Pattern Recognition Approach for Transferring Knowledge in ACM
Authors: Kim, Thanh Tran Thi 
Ruhsam, Christoph 
Pucher, Max J. 
Kobler, Maximilian 
Mendling, Jan 
Keywords: ACM;pattern recognition;adaptive system;decision support system;UTA;user trained agent
Issue Date: 1-Sep-2014
Source: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations
Abstract: In Adaptive Case Management (ACM) systems, knowledge workers have the flexibility to deal with unpredictable situations. Compared with a classical BPM approach the extensive prescriptive process analysis and definitions are replaced by context-sensitive proposals, which is more suited for knowledge-intensive work. Thus, it is vital that ACM systems support knowledge workers with knowledge captured from previous work which can be ambiguous for the system. This paper proposes an approach to support knowledge workers based on the knowledge previously applied by others in the form of a User Trained Agent that learns from ad hoc actions taken by knowledge workers to suggest best next actions for the current situation. The proposed best next actions are analyzed for coherence.
URI: http://hdl.handle.net/20.500.11790/340
ISSN: 2325-6583
DOI: 10.1109/EDOCW.2014.28
Rights: info:eu-repo/semantics/openAccess
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