Version 1
: Received: 24 July 2024 / Approved: 25 July 2024 / Online: 25 July 2024 (07:22:08 CEST)
How to cite:
Težak, O.; Hercog, D.; Truntič, M.; Žmauc, T. Human-Inspired Decision-Making in AI: Leveraging Insights from Visual Perception for Real-Time Sensor Data Processing. Preprints2024, 2024072012. https://doi.org/10.20944/preprints202407.2012.v1
Težak, O.; Hercog, D.; Truntič, M.; Žmauc, T. Human-Inspired Decision-Making in AI: Leveraging Insights from Visual Perception for Real-Time Sensor Data Processing. Preprints 2024, 2024072012. https://doi.org/10.20944/preprints202407.2012.v1
Težak, O.; Hercog, D.; Truntič, M.; Žmauc, T. Human-Inspired Decision-Making in AI: Leveraging Insights from Visual Perception for Real-Time Sensor Data Processing. Preprints2024, 2024072012. https://doi.org/10.20944/preprints202407.2012.v1
APA Style
Težak, O., Hercog, D., Truntič, M., & Žmauc, T. (2024). Human-Inspired Decision-Making in AI: Leveraging Insights from Visual Perception for Real-Time Sensor Data Processing. Preprints. https://doi.org/10.20944/preprints202407.2012.v1
Chicago/Turabian Style
Težak, O., Mitja Truntič and Tanja Žmauc. 2024 "Human-Inspired Decision-Making in AI: Leveraging Insights from Visual Perception for Real-Time Sensor Data Processing" Preprints. https://doi.org/10.20944/preprints202407.2012.v1
Abstract
This paper introduces a novel decision-making approach grounded in insights into human visual perception of change. Modern technologies such as internet of things (IoT) provide us with large amounts of sensor data that need to be processed in real time and decisions made with a high degree of accuracy and reliability. Artificial intelligence (AI) methods are welcome in this context and need to be upgraded to meet actual challenges. While modern computing capabilities facilitate rapid data processing, the real-time demands of vast sensor data necessitate swift responses across the cyber chain, often leading to compromises in solution quality to circumvent combinatorial search complexities. Determining the adequacy of a solution entails varied approaches, often relying on heuristic methodologies. We illustrate our original approach with an example of a selected detail of a differential evolution algorithm, where we have to make a decision to adopt the best solution so far. We propose an approach inspired by human perceptual features that exploits the Weber-Fechner law to emulate human judgements, and offers a promising way to improve decision making in AI applications and real-time requirements fulfillment. Our proposed methodology demonstrates applicability across diverse AI scenarios involving numerical data, effectively mirroring human perception abilities.
Keywords
AI; differential evolution; real-time systems; psychophysics; Weber-Fechner law
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
