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 editorsMishra, Sunny; Shukla, Amit K.; Muhuri, Pranab K.

Journal or seriesAxioms


Publication year2022

Publication date20/09/2022


Issue number10

Article number489

PublisherMDPI AG

Publication countrySwitzerland

Publication languageEnglish


Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/84292


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.

Keywordsartificial intelligenceintelligent agentsinferenceoptimisationalgorithmicsfuzzy logic

Free keywordsexplainable AI; fuzzy systems; AI agents; Mamdani inference system; TSK

Contributing organizations

Ministry reportingYes

Reporting Year2022

JUFO rating1

Last updated on 2024-15-06 at 01:06