Case Completion of Workflows for Process-Oriented Case-Based Reasoning.
Müller, G.; and Bergmann, R.
In Goel, A.; Diaz-Agudo, B.; and Roth-Berghofer, T., editor(s),
Case-Based Reasoning Research and Development - 24nd International Conference, ICCBR 2016, Atlanta, USA. Proceedings, volume 9969, of
Lecture Notes in Artificial Intelligence, pages 295–310, 2016. Springer
The original publication is available at www.springerlink.com
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@inproceedings{muller_case_2016,
series = {Lecture {Notes} in {Artificial} {Intelligence}},
title = {Case {Completion} of {Workflows} for {Process}-{Oriented} {Case}-{Based} {Reasoning}},
volume = {9969},
url = {http://www.wi2.uni-trier.de/publications/2016_MuellerBergmann_ICCBR.pdf},
abstract = {Cases available in real world domains are often incomplete and sometimes
lack important information. Using incomplete cases in a CBR system can
be harmful, as the lack of information can result in inappropriate similarity computations
or incompletely generated adaptation knowledge. Case completion aims
to overcome this issue by inferring missing information. This paper presents
a novel approach to case completion for process-oriented case-based reasoning
(POCBR). In particular, we address the completion of workflow cases by
adding missing or incomplete dataflow information. Therefore, we combine automatically
learned domain specific completion operators with generic domainindependent
default rules. The empirical evaluation demonstrates that the presented
completion approach is capable of deriving complete workflows with high
quality and a high degree of completeness.},
booktitle = {Case-{Based} {Reasoning} {Research} and {Development} - 24nd {International} {Conference}, {ICCBR} 2016, {Atlanta}, {USA}. {Proceedings}},
publisher = {Springer},
author = {Müller, Gilbert and Bergmann, Ralph},
editor = {Goel, A. and Diaz-Agudo, B. and Roth-Berghofer, Thomas},
year = {2016},
note = {The original publication is available at www.springerlink.com},
pages = {295--310}
}
Cases available in real world domains are often incomplete and sometimes lack important information. Using incomplete cases in a CBR system can be harmful, as the lack of information can result in inappropriate similarity computations or incompletely generated adaptation knowledge. Case completion aims to overcome this issue by inferring missing information. This paper presents a novel approach to case completion for process-oriented case-based reasoning (POCBR). In particular, we address the completion of workflow cases by adding missing or incomplete dataflow information. Therefore, we combine automatically learned domain specific completion operators with generic domainindependent default rules. The empirical evaluation demonstrates that the presented completion approach is capable of deriving complete workflows with high quality and a high degree of completeness.
Retrieving Adaptable Cases in Process-Oriented Case-Based Reasoning.
Bergmann, R.; Müller, G.; Zeyen, C.; and Manderscheid, J.
In Markov, Z.; and Russel, I., editor(s),
Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, pages 419–424, Key Largo, Florida, USA, 2016. AAAI Press
Paper
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bibtex
abstract
7 downloads
@inproceedings{bergmann_retrieving_2016,
address = {Key Largo, Florida, USA},
title = {Retrieving {Adaptable} {Cases} in {Process}-{Oriented} {Case}-{Based} {Reasoning}},
url = {http://www.wi2.uni-trier.de/publications/2016_Bergmann_Flairs.pdf},
abstract = {This paper presents a novel approach to retrieval in
process-oriented case-based reasoning (POCBR) which
considers the adaptability of workflows cases during
the retrieval phase. A novel concept of adaptability in
POCBR is proposed, which assesses the potential similarity
increase of a case which can be gained by adaptation.
The adaptability of a case is learned from the case
base in an off-line pre-processing phase prior to the retrieval.
The proposed approach is generic as it can be
used in combination with different adaptation methods.
An empirical evaluation in the domain of cooking workflows
demonstrates the benefit of the approach.},
booktitle = {Proceedings of the {Twenty}-{Ninth} {International} {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}},
publisher = {AAAI Press},
author = {Bergmann, Ralph and Müller, Gilbert and Zeyen, Christian and Manderscheid, Jens},
editor = {Markov, Zdravko and Russel, Ingrid},
year = {2016},
pages = {419--424}
}
This paper presents a novel approach to retrieval in process-oriented case-based reasoning (POCBR) which considers the adaptability of workflows cases during the retrieval phase. A novel concept of adaptability in POCBR is proposed, which assesses the potential similarity increase of a case which can be gained by adaptation. The adaptability of a case is learned from the case base in an off-line pre-processing phase prior to the retrieval. The proposed approach is generic as it can be used in combination with different adaptation methods. An empirical evaluation in the domain of cooking workflows demonstrates the benefit of the approach.
EVER - Extraction and Processing of Procedural Experience Knowledge in Workflows.
Bergmann, R.; Minor, M.; Müller, G.; and Schumacher, P.
Technical Report Universität Trier, Lehrstuhl für Wirtschaftsinformatik II, 2016.
link
bibtex
@techreport{bergmann_ever_2016,
title = {{EVER} - {Extraction} and {Processing} of {Procedural} {Experience} {Knowledge} in {Workflows}},
institution = {Universität Trier, Lehrstuhl für Wirtschaftsinformatik II},
author = {Bergmann, Ralph and Minor, Mirjam and Müller, Gilbert and Schumacher, Pol},
year = {2016},
pages = {1--9}
}