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Authors: Kristin Stamm and Andreas Dengel

Affiliation: German Reseach Center for Artificial Intelligence and University Kaiserslautern, Germany

ISBN: 978-989-8565-39-6

Keyword(s): Machine Learning, Enterprise Applications, Evidence based Search, Information Gain Tree, Search Failure Detection, Degree of Belief, Dempster Shafer Theory, Multichannel Document Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Industrial Applications of AI ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Enterprises today are challenged by managing requests arriving through all communication channels. To support service employees in better and faster understanding incoming documents, we developed the approach of process-driven document analysis (DA). We introduced the structure Attentive Task (AT) to formalize information expectations toward an incoming document. To map the documents to the corresponding AT, we previously developed a novel search approach that uses DA results as evidences for prioritizing all AT. With this approach, we consider numerous task instances including their context instead of a few process classes. The application of AT search in enterprises raises two challenges: (1) Complex domains require a structured selection of well performing evidence types, (2) a failure detection method is needed for handling a substantial part of incoming documents that cannot be related to any AT. Here, we apply methods from machine learning to meet these requirements. We learn an d apply information gain trees for structuring and optimizing evidence selection. We propose five strategies for detecting documents without ATs. We evaluate the suggested methods with two processes of a financial institution. (More)

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Paper citation in several formats:
Stamm K. and Dengel A. (2013). Enhancing Attentive Task Search with Information Gain Trees and Failure Detection Strategies.In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 81-90. DOI: 10.5220/0004227300810090

@conference{icaart13,
author={Kristin Stamm and Andreas Dengel},
title={Enhancing Attentive Task Search with Information Gain Trees and Failure Detection Strategies},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004227300810090},
isbn={978-989-8565-39-6},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Enhancing Attentive Task Search with Information Gain Trees and Failure Detection Strategies
SN - 978-989-8565-39-6
AU - Stamm K.
AU - Dengel A.
PY - 2013
SP - 81
EP - 90
DO - 10.5220/0004227300810090

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