Decision Maker based on Atomic Switches
Song-Ju Kim, Tohru Tsuruoka, Tsuyoshi Hasegawa, and Masakazu Aono

TL;DR
This paper introduces an atomic switch-based decision maker (ASDM) that leverages conservation laws and tug-of-war dynamics to efficiently solve multi-armed bandit problems, offering a new physics-inspired approach to decision-making.
Contribution
The paper presents a novel atomic switch model for decision-making that outperforms traditional methods and provides analytical validation of its efficiency based on physical conservation laws.
Findings
ASDM exhibits higher efficiency than conventional MAB solvers.
Analytical calculations support the statistical basis of ASDM's performance.
Physical systems with conservation laws can be used for decision-making applications.
Abstract
We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total volume of precipitated Ag atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated "decision-making" capability that is known to be one of the most important intellectual abilities in human beings. We considered the multi-armed bandit problem (MAB); the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. These decisions are made as dictated by each volume of precipitated Ag atoms, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war game. The "tug-of-war (TOW) dynamics" of the ASDM exhibits higher efficiency than conventional MAB solvers. We show analytical calculations that validate the statistical reasons…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Bandit Algorithms Research · Neural Networks and Reservoir Computing · Quantum Computing Algorithms and Architecture
