Research article Special Issues

Decision maker based on atomic switches

  • Received: 12 July 2015 Accepted: 22 February 2016 Published: 25 February 2016
  • We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total number of metal 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 a popular decision-making problem studied in the context of reinforcement learning, 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 metal 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 reinforcement-learning algorithms. We show analytical calculations that validate the statistical reasons for the ASDM to produce such high performance, despite its simplicity. Efficient MAB solvers are useful for many practical applications, because MAB abstracts a variety of decision-making problems in real-world situations where an efficient trial-and-error is required. The proposed scheme will open up a new direction in physics-based analog-computing paradigms, which will include such things as ``intelligent nanodevices'' based on self-judgment.

    Citation: Song-Ju Kim, Tohru Tsuruoka, Tsuyoshi Hasegawa, Masashi Aono, Kazuya Terabe, Masakazu Aono. Decision maker based on atomic switches[J]. AIMS Materials Science, 2016, 3(1): 245-259. doi: 10.3934/matersci.2016.1.245

    Related Papers:

  • We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total number of metal 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 a popular decision-making problem studied in the context of reinforcement learning, 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 metal 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 reinforcement-learning algorithms. We show analytical calculations that validate the statistical reasons for the ASDM to produce such high performance, despite its simplicity. Efficient MAB solvers are useful for many practical applications, because MAB abstracts a variety of decision-making problems in real-world situations where an efficient trial-and-error is required. The proposed scheme will open up a new direction in physics-based analog-computing paradigms, which will include such things as ``intelligent nanodevices'' based on self-judgment.


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    [1] Castro LN (2007) Fundamentals of natural computing: an overview. Physics of Life Reviews 4: 1-36. doi: 10.1016/j.plrev.2006.10.002
    [2] Kari L, Rozenberg G (2008) The many facets of natural computing. Communications of the ACM 51: 72-83.
    [3] Rozenberg G, Back T, Kok J (2012) Handbook of natural computing, Springer-Verlag.
    [4] Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization in simulated annealing. Science 220: 671-680. doi: 10.1126/science.220.4598.671
    [5] Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biological Cybernetics 52: 141-152.
    [6] Brady RM (1985) Optimization strategies gleaned from biological evolution. Nature 317: 804-806. doi: 10.1038/317804a0
    [7] Adelman LM (1994) Molecular computation of solutions to combinatorial problems. Science 266: 1021-1024. doi: 10.1126/science.7973651
    [8] Terabe K, Hasegawa T, Nakayama T, et al. (2001) Quantum point contact switch realized by solid electrochemical reaction. RIKEN Review 37: 7-8.
    [9] Terabe K, Hasegawa T, Nakayama T, et al. (2005) Quantized conductance atomic switch. Nature 433: 47-50. doi: 10.1038/nature03190
    [10] Kim S-J, Aono M, Nameda E (2015) Efficient decision-making by volume-conserving physical object. New J Phys 17: 083023. doi: 10.1088/1367-2630/17/8/083023
    [11] Robbins H (1952) Some aspects of the sequential design of experiments. Bull Amer Math Soc 58: 527-536. doi: 10.1090/S0002-9904-1952-09620-8
    [12] Thompson W (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25: 285-294. doi: 10.1093/biomet/25.3-4.285
    [13] Gittins J, Jones D (1974) Dynamic allocation index for the sequential design of experiments, In: Gans, J. Progress in Statistics North Holland, 241-266.
    [14] Gittins J (1979) Bandit processes and dynamic allocation indices. J R Stat Soc B 41: 148-177.
    [15] Agrawal R (1995) Sample mean based index policies with O(log n) regret for the multi-armed bandit problem. Adv Appl Prob 27: 1054-1078. doi: 10.2307/1427934
    [16] Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learning 47: 235-256. doi: 10.1023/A:1013689704352
    [17] Lai L, Jiang H, Poor HV (2008) Medium access in cognitive radio networks: a competitive multiarmed bandit framework. Proc. of IEEE 42nd Asilomar Conference on Signals, System and Computers, 98-102.
    [18] Lai L, Gamal HE, Jiang H, et al. (2011) Cognitive medium access: exploration, exploitation, and competition. IEEE Trans. on Mobile Computing 10: 239-253. doi: 10.1109/TMC.2010.65
    [19] Agarwal D, Chen BC, Elango P (2009) Explore/exploit schemes for web content optimization. Proc of ICDM2009, http://dx.doi.org/10.1109/ICDM.2009.52.
    [20] Kocsis L, Szepesv´ari C. (2006) Bandit based monte-carlo planning, In: Carbonell, J. G. et al., 17th European Conference on Machine Learning, Lecture Notes in Artificial Intelligence 4212, Springer, 282-293.
    [21] Gelly S, Wang Y, Munos R, et al. (2006) Modification of UCT with patterns in Monte-Carlo Go. RR-6062-INRIA, 1-19.
    [22] Demis EC, Aguilera R, Sillin HO, et al. (2015) Atomic switch networks - nanoarchitectonic design of a complex system for natural computing. Nanotechnology 26: 204003. doi: 10.1088/0957-4484/26/20/204003
    [23] Avizienis AV, Sillin HO, Martin-Olmos C, et al. (2012) Neuromorphic atomic switch networks. PLoS ONE 7: e42772. doi: 10.1371/journal.pone.0042772
    [24] Sutton R, Barto A (1998) Reinforcement Learning: An Introduction, MIT Press.
    [25] Kim S-J, Aono M, Hara M (2010) Tug-of-war model for multi-armed bandit problem, In: Calude C. et al. Unconventional Computation, Lecture Notes in Computer Science 6079, Springer, 69-80.
    [26] Kim S-J, Aono M, Hara M (2010) Tug-of-war model for the two-bandit problem: Nonlocallycorrelated parallel exploration via resource conservation. BioSystems 101: 29-36. doi: 10.1016/j.biosystems.2010.04.002
    [27] Kim S-J, Naruse M, Aono M, et al. (2013) Decision maker based on nanoscale photo-excitation transfer. Sci Rep 3: 2370. doi: 10.1038/srep02370
    [28] Naruse M, NomuraW, Aono M, et al. (2014) Decision making based on optical excitation transfer via near-field interactions between quantum dots. J Appl Phys 116: 154303. doi: 10.1063/1.4898570
    [29] Naruse M, Berthel M, Drezet A, et al. (2015) Single photon decision maker Sci Rep 5: 13253. doi: 10.1038/srep13253
    [30] Tsuruoka T, Hasegawa T, Terabe K, et al. (2012) Conductance quantization and synaptic behavior in a Ta2O5-based atomic switch. Nanotechnology 23: 435705. doi: 10.1088/0957-4484/23/43/435705
    [31] Roughgarden T (2005) Selfish routing and the price of anarchy, MIT Press, Cambridge.
    [32] Nisan N, Roughgarden T, Tardos E, et al. (2007) Algorithmic Game Theory, Cambridge Univ. Press.
    [33] Kim S-J, Aono M (2015) Decision maker using coupled incompressible-fluid cylinders. Special issue of Advances in Science, Technology and Environmentology B11: 41-45, Available from: http://arxiv.org/abs/1502.03890.
    [34] Kim S-J, Naruse M, Aono M (2015) Harnessing natural fluctuations: analogue computer for efficient socially-maximal decision-making. eprint arXiv, Available from: http://arxiv.org/abs/1504.03451.
    [35] Vermorel J, Mohri M (2005) Multi-armed bandit algorithms and empirical evaluation, In: Gama J., et al. 16th European Conference on Machine Learning. Lecture Notes in Artificial Intelligence 3720, Springer, 437-448.
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