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A Survey of Non-Learning-Based Abstractions for Sequential Decision-Making

Research output: Contribution to journalArticleResearchpeer review

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Original languageEnglish
Pages (from-to)100808-100830
Number of pages23
JournalIEEE ACCESS
Volume13
Publication statusPublished - 22 May 2025

Abstract

Abstraction techniques play a crucial role in enabling agents to make decisions more effectively by simplifying complex problems. This survey provides a comprehensive literature overview of non-learned abstraction construction methods and explores how these abstractions can enhance or be seamlessly integrated into existing solvers. We delve into key properties of abstractions, outline general strategies for constructing them, and discuss specialized approaches for specific problem domains, such as those with continuous action spaces. Additionally, we introduce the Abstraction Mapping Graph (AMG) framework, offering a structured lens through which abstraction usage can be systematically understood.

Keywords

    Abstractions, artificial intelligence, sequential decision-making

ASJC Scopus subject areas

Cite this

A Survey of Non-Learning-Based Abstractions for Sequential Decision-Making. / Schmöcker, Robin; Dockhorn, Alexander.
In: IEEE ACCESS, Vol. 13, 22.05.2025, p. 100808-100830.

Research output: Contribution to journalArticleResearchpeer review

Schmöcker R, Dockhorn A. A Survey of Non-Learning-Based Abstractions for Sequential Decision-Making. IEEE ACCESS. 2025 May 22;13:100808-100830. doi: 10.1109/ACCESS.2025.3572830
Schmöcker, Robin ; Dockhorn, Alexander. / A Survey of Non-Learning-Based Abstractions for Sequential Decision-Making. In: IEEE ACCESS. 2025 ; Vol. 13. pp. 100808-100830.
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