Intentional forgetting in artificial intelligence systems

Perspectives and challenges

verfasst von: Ingo J. Timm, Steffen Staab, Michael Siebers, Claudia Schon, Ute Schmid, Kai Sauerwald, Lukas Reuter, Marco Ragni, Claudia Niederée, Heiko Maus, Gabriele Kern-Isberner, Christian Jilek, Paulina Friemann, Thomas Eiter, Andreas Dengel, Hannah Dames, Tanja Bock, Jan Ole Berndt, Christoph Beierle
Abstract: Current trends, like digital transformation and ubiquitous computing, yield in massive increase in available data and information. In artificial intelligence (AI) systems, capacity of knowledge bases is limited due to computational complexity of many inference algorithms. Consequently, continuously sampling information and unfiltered storing in knowledge bases does not seem to be a promising or even feasible strategy. In human evolution, learning and forgetting have evolved as advantageous strategies for coping with available information by adding new knowledge to and removing irrelevant information from the human memory. Learning has been adopted in AI systems in various algorithms and applications. Forgetting, however, especially intentional forgetting, has not been sufficiently considered, yet. Thus, the objective of this paper is to discuss intentional forgetting in the context of AI systems as a first step. Starting with the new priority research program on ‘Intentional Forgetting’ (DFG-SPP 1921), definitions and interpretations of intentional forgetting in AI systems from different perspectives (knowledge representation, cognition, ontologies, reasoning, machine learning, self-organization, and distributed AI) are presented and opportunities as well as challenges are derived.
Organisationseinheit(en): Forschungszentrum L3S
Externe Organisation(en): Universität Trier
Universität Koblenz-Landau
Otto-Friedrich-Universität Bamberg
FernUniversität in Hagen
Albert-Ludwigs-Universität Freiburg
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
Technische Universität Dortmund
Technische Universität Wien (TUW)
Typ: Aufsatz in Konferenzband
Seiten: 357-365
Anzahl der Seiten: 9
Publikationsdatum: 2018
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Theoretische Informatik, Allgemeine Computerwissenschaft
Elektronische Version(en): https://doi.org/10.1007/978-3-030-00111-7_30 (Zugang: Geschlossen)

BibTeX

@inproceedings{a0d871f46baf4c619499a04c8bb8239a,
title = "Intentional forgetting in artificial intelligence systems: Perspectives and challenges",
abstract = "Current trends, like digital transformation and ubiquitous computing, yield in massive increase in available data and information. In artificial intelligence (AI) systems, capacity of knowledge bases is limited due to computational complexity of many inference algorithms. Consequently, continuously sampling information and unfiltered storing in knowledge bases does not seem to be a promising or even feasible strategy. In human evolution, learning and forgetting have evolved as advantageous strategies for coping with available information by adding new knowledge to and removing irrelevant information from the human memory. Learning has been adopted in AI systems in various algorithms and applications. Forgetting, however, especially intentional forgetting, has not been sufficiently considered, yet. Thus, the objective of this paper is to discuss intentional forgetting in the context of AI systems as a first step. Starting with the new priority research program on {\textquoteleft}Intentional Forgetting{\textquoteright} (DFG-SPP 1921), definitions and interpretations of intentional forgetting in AI systems from different perspectives (knowledge representation, cognition, ontologies, reasoning, machine learning, self-organization, and distributed AI) are presented and opportunities as well as challenges are derived.",
keywords = "Artificial intelligence systems, Capacity and efficiency of knowledge-based systems (Intentional)",
author = "Timm, {Ingo J.} and Steffen Staab and Michael Siebers and Claudia Schon and Ute Schmid and Kai Sauerwald and Lukas Reuter and Marco Ragni and Claudia Nieder{\'e}e and Heiko Maus and Gabriele Kern-Isberner and Christian Jilek and Paulina Friemann and Thomas Eiter and Andreas Dengel and Hannah Dames and Tanja Bock and Berndt, {Jan Ole} and Christoph Beierle",
note = "Funding information: Acknowledgments. The authors are indebted to the DFG for funding this The authors are indebted to the DFG for funding this research: Dare2Del (SCHM1239/10-1), EVOWIPE (STA572/15-1), FADE (BE 1700/9-1, KE1413/10-1, RA1934/5-1), Managed Forgetting (DE420/19-1, NI1760 1-1), and AdaptPro (TI548/5-1). We would also like to thank our project partners for their fruitful discussion: C. Antoni, T. Ellwart, M. Feuerbach, C. Frings, K. G{\"o}bel, P. K{\"u}gler, C. Niessen, Y. Runge, T. Tempel, A. Ulfert, S. Wartzack.; 41st German Conference on Artificial Intelligence, KI 2018 ; Conference date: 24-09-2018 Through 28-09-2018",
year = "2018",
doi = "10.1007/978-3-030-00111-7_30",
language = "English",
isbn = "9783030001100",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "357--365",
editor = "Anni-Yasmin Turhan and Frank Trollmann",
booktitle = "KI 2018",
address = "Germany",
}