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I. Timm, S. Staab, M. Siebers, C. Schon, U. Schmid, K. Sauerwald, L. Reuter, M. Ragni, C. Niederee, H. Maus, G. Kern-Isberner, T. Eiter, C. Jilek, P. Friemann, A. Dengel, H. Dames, T. Bock, J. Berndt, C. Beierle:
"Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges";
in: "Proceedings of the 41st German Conference on AI (KI 2018), Berlin, Germany, September 24-28, 2018", 11117; Springer, 2018, ISBN: 978-3-030-00110-0, 357 - 365.

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.

Artificial intelligence systems, Capacity and efficiency of knowledge-based systems (Intentional)

http://dx.doi.org/10.1007/978-3-030-00111-7