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Related Experiment Video

Updated: Jun 6, 2026

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

Updating action domain descriptions.

Thomas Eiter1, Esra Erdem, Michael Fink

  • 1Institute of Information Systems, Vienna University of Technology, Vienna, Austria.

Artificial Intelligence
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a formal framework for updating action domain descriptions with new information, enabling more general and preference-based updates for reasoning about actions and change.

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The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
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The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Related Experiment Videos

Last Updated: Jun 6, 2026

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Area of Science:

  • Artificial Intelligence
  • Knowledge Representation and Reasoning

Background:

  • Incorporating new information into knowledge bases is a critical challenge in AI.
  • Existing formalisms often lack the expressivity to handle complex updates.

Purpose of the Study:

  • To develop a formal framework for updating action domain descriptions.
  • To enable more general updates considering non-deterministic and indirect effects.
  • To incorporate preference criteria for selecting optimal updates.

Main Methods:

  • Utilizing an action language with causality-based semantics.
  • Representing non-deterministic effects, indirect effects, and state constraints.
  • Developing semantic and computational analyses of the update problem.
  • Investigating preference relations for update selection.

Main Results:

  • A general approach to updating action descriptions with additional constraints.
  • A decomposition theorem for a divide-and-conquer update strategy.
  • Analysis of computational complexity, with general problems being PSPACE-complete.
  • Identification of methods for computing exact and approximate solutions.

Conclusions:

  • The proposed framework provides a robust semantic and computational basis for updating action domain descriptions.
  • It supports incorporating diverse information and preferences, advancing reasoning systems.
  • The findings facilitate the development of AI systems that dynamically learn and adapt.