A1 Journal article (refereed)
Explainable Fuzzy AI Challenge 2022 : Winner’s Approach to a Computationally Efficient and Explainable Solution (2022)
Mishra, S., Shukla, A. K., & Muhuri, P. K. (2022). Explainable Fuzzy AI Challenge 2022 : Winner’s Approach to a Computationally Efficient and Explainable Solution. Axioms, 11(10), Article 489. https://doi.org/10.3390/axioms11100489
JYU authors or editors
Publication details
All authors or editors: Mishra, Sunny; Shukla, Amit K.; Muhuri, Pranab K.
Journal or series: Axioms
eISSN: 2075-1680
Publication year: 2022
Publication date: 20/09/2022
Volume: 11
Issue number: 10
Article number: 489
Publisher: MDPI AG
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.3390/axioms11100489
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84292
Abstract
An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based fuzzy systems. We also discuss the proposed approach (which won the competition) to attain efficiency in the XAI algorithm. We have explored the potential of the widely used Mamdani- and TSK-based fuzzy inference systems and investigated which model might have a more optimized implementation. Even though the TSK-based model outperforms Mamdani in several applications, no empirical evidence suggests this will also be applicable in implementing an XAI agent. The experimentations are then performed to find a better-performing inference system in a fast-paced environment. The thorough analysis recommends more robust and efficient TSK-based XAI agents than Mamdani-based fuzzy inference systems.
Keywords: artificial intelligence; intelligent agents; inference; optimisation; algorithmics; fuzzy logic
Free keywords: explainable AI; fuzzy systems; AI agents; Mamdani inference system; TSK
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 1