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Intentional Forgetting Operators—Formal Foundations and Empirical Support

In: Intentional Forgetting with Intelligent Systems

Author

Listed:
  • Christoph Beierle

    (FernUniversität in Hagen, Faculty of Mathematics and Computer Science)

  • Daniel Brand

    (TU Chemnitz, Predictive Analytics)

  • Diana Howey

    (TU Dortmund University, Department of Computer Science)

  • Gabriele Kern-Isberner

    (TU Dortmund University, Department of Computer Science)

  • Kai Sauerwald

    (FernUniversität in Hagen, Faculty of Mathematics and Computer Science)

  • Sara Todorovikj

    (TU Chemnitz, Predictive Analytics)

  • Marco Ragni

    (TU Chemnitz, Predictive Analytics)

Abstract

This chapter presents a framework for understanding intentional forgetting processes in humans that is jointly grounded in cognitive psychology and knowledge representation. It provides formal modeling structures, experimental designs, and best implementation practices. We identify kinds of forgetting, axiomatically formalize belief change operators involving forgetting, and elaborate their interrelationships. This general abstract framework is instantiated with conditional beliefs and realizations of forgetting operators employing ranking functions. On a cognitive level, we examine the cognitive mechanisms of intentional forgetting experimentally by employing the counting game paradigm. Our results show that changes to conditionals in procedural memory leave behind artifacts, which can improve performance when old and new conditionals align.

Suggested Citation

  • Christoph Beierle & Daniel Brand & Diana Howey & Gabriele Kern-Isberner & Kai Sauerwald & Sara Todorovikj & Marco Ragni, 2026. "Intentional Forgetting Operators—Formal Foundations and Empirical Support," Progress in IS, in: Jan Ole Berndt & Marcus Grum & Kyra Göbel & Annette Kluge (ed.), Intentional Forgetting with Intelligent Systems, pages 129-157, Springer.
  • Handle: RePEc:spr:prochp:978-3-032-17621-9_7
    DOI: 10.1007/978-3-032-17621-9_7
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