Stochastic Learning in Kolkata Paise Restaurant Problem: Classical and Quantum Strategies

Frontiers in Artificial Intelligence

Antika Sinha²  Atanu Rajak³  Bikas K. Chakrabarti⁴ 

[1] Condensed Matter Physics Division, Saha Institute of Nuclear Physics, Kolkata, India;Department of Computer Science, Asutosh College, Kolkata, India;Department of Physics, Presidency University, Kolkata, India;Economic Research Unit, Indian Statistical Institute, Kolkata, India;S. N. Bose National Centre for Basic Sciences, Kolkata, India;

关键词: collective learning;    critical slowing down;    decoherence;    KPR problem;    minority game;    quantum entanglement;   

DOI  :  10.3389/frai.2022.874061

来源: DOAJ

【 摘 要 】

We review the results for stochastic learning strategies, both classical (one-shot and iterative) and quantum (one-shot only), for optimizing the available many-choice resources among a large number of competing agents, developed over the last decade in the context of the Kolkata Paise Restaurant (KPR) Problem. Apart from few rigorous and approximate analytical results, both for classical and quantum strategies, most of the interesting results on the phase transition behavior (obtained so far for the classical model) uses classical Monte Carlo simulations. All these including the applications to computer science [job or resource allotments in Internet-of-Things (IoT)], transport engineering (online vehicle hire problems), operation research (optimizing efforts for delegated search problem, efficient solution of Traveling Salesman problem) will be discussed.

【 授权许可】

Unknown