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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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Mishra, Sunny; Shukla, Amit K.; Muhuri, Pranab K.
Lehti tai sarja: Axioms
eISSN: 2075-1680
Julkaisuvuosi: 2022
Ilmestymispäivä: 20.09.2022
Volyymi: 11
Lehden numero: 10
Artikkelinumero: 489
Kustantaja: MDPI AG
Julkaisumaa: Sveitsi
Julkaisun kieli: englanti
DOI: https://doi.org/10.3390/axioms11100489
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/84292
Tiivistelmä
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.
YSO-asiasanat: tekoäly; älykkäät agentit; päättely; optimointi; algoritmiikka; sumea logiikka
Vapaat asiasanat: explainable AI; fuzzy systems; AI agents; Mamdani inference system; TSK
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2022
JUFO-taso: 1