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Related Experiment Video

Updated: Jul 7, 2026

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

Asymmetry in search.

H Kaindl¹, G Kainz, K Radda

¹Siemens AG, Wien.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This summary is machine-generated.

This study introduces a new AI search algorithm that dynamically selects search direction. It significantly improves efficiency in job sequencing problems by exploiting search graph asymmetries.

Related Experiment Videos

Last Updated: Jul 7, 2026

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

Area of Science:

Artificial Intelligence
Computer Science
Operations Research

Background:

Traditional AI search algorithms often focus on a single direction (e.g., forward search).
Search efficiency can be impacted by the structural asymmetry of the search graph.
Current theory does not predict efficiency differences for symmetrical search graphs.

Purpose of the Study:

To investigate search asymmetry in job sequencing problems.
To develop a novel algorithm that leverages search graph properties for improved efficiency.
To demonstrate enhanced problem-solving capabilities and reduced runtime.

Main Methods:

Observation of search asymmetry related to cost distribution in job sequencing problem graphs.
Development of an adaptive search algorithm that automatically selects the optimal search direction.
Empirical evaluation on challenging job sequencing problem instances.

Main Results:

Identified a phenomenon of search asymmetry linked to cost distribution in job sequencing problems.
The new algorithm significantly outperforms existing state-of-the-art approaches in solving difficult instances.
Demonstrated substantial reductions in computational runtime for the tested problem class.

Conclusions:

Search graph asymmetry, driven by cost distribution, is a critical factor for AI search efficiency.
An adaptive search direction selection algorithm can overcome limitations of unidirectional search.
Future work should involve checking problems for cost-based asymmetry to guide search strategy.