Intentional forgetting in artificial intelligence systems

Perspectives and challenges

authored by: 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.
Organisation(s): L3S Research Centre
External Organisation(s): Trier University
University of Koblenz-Landau
University of Bamberg
FernUniversität in Hagen
University of Freiburg
German Research Centre for Artificial Intelligence (DFKI)
TU Dortmund University
TU Wien (TUW)
Type: Conference contribution
Pages: 357-365
No. of pages: 9
Publication date: 2018
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Theoretical Computer Science, General Computer Science
Electronic version(s): https://doi.org/10.1007/978-3-030-00111-7_30 (Access: Closed)

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",
}