Open AccessA survey of non-learning-based abstractions for sequential decision-making
Author(s) -
Robin Schmocker,
Alexander Dockhorn
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3572830
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.