Explainable Fuzzy AI Challenge 2022 : Winner’s Approach to a Computationally Efficient and Explainable Solution
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
DisciplineLaskennallinen tiedeComputing, Information Technology and MathematicsComputational ScienceComputing, Information Technology and Mathematics
© 2022 by the authors. Licensee MDPI, Basel, Switzerland
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. ...
Publication in research information system
MetadataShow full item record
Additional information about fundingThis research received no external funding.
Showing items with similar title or keywords.
Self-management in distributed systems : smart adaptive framework for pervasive computing environments Nagy, Michal (University of Jyväskylä, 2013)
Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture Afsar, Bekir; Podkopaev, Dmitry; Miettinen, Kaisa (Elsevier BV, 2020)In many decision making problems, a decision maker needs computer support in finding a good compromise between multiple conflicting objectives that need to be optimized simultaneously. Interactive multiobjective optimization ...
Karvonen, Antero (2018)Tutkielman tarkoitus on arvioida autonomisen teknologian ja keinoälyn taustalle vallitsevia olettamuksia perusteanalyyttisestä näkökulmasta käyttäen autonomisia laivoja kontekstina. Teoreettinen tutkielma on kriittinen, ...
Iacca, Giovanni (University of Jyväskylä, 2011)
Nikitin, Sergiy (University of Jyväskylä, 2011)