Tug-of-war model for the two-bandit problem: Nonlocally-correlated parallel exploration via resource conservation

Song Ju Kim, Masashi Aono, Masahiko Hara

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

We propose a model - the " tug-of-war (TOW) model" - to conduct unique parallel searches using many nonlocally-correlated search agents. The model is based on the property of a single-celled amoeba, the true slime mold Physarum, which maintains a constant intracellular resource volume while collecting environmental information by concurrently expanding and shrinking its branches. The conservation law entails a " nonlocal correlation" among the branches, i.e., volume increment in one branch is immediately compensated by volume decrement(s) in the other branch(es). This nonlocal correlation was shown to be useful for decision making in the case of a dilemma. The multi-armed bandit problem is to determine the optimal strategy for maximizing the total reward sum with incompatible demands, by either exploiting the rewards obtained using the already collected information or exploring new information for acquiring higher payoffs involving risks. Our model can efficiently manage the " exploration-exploitation dilemma" and exhibits good performances. The average accuracy rate of our model is higher than those of well-known algorithms such as the modified ε{lunate}-greedy algorithm and modified softmax algorithm, especially, for solving relatively difficult problems. Moreover, our model flexibly adapts to changing environments, a property essential for living organisms surviving in uncertain environments.

Original languageEnglish
Pages (from-to)29-36
Number of pages8
JournalBioSystems
Volume101
Issue number1
DOIs
Publication statusPublished - 2010 Jul 1
Externally publishedYes

Keywords

  • Amoeba-based computing
  • Bio-inspired computing
  • Multi-armed bandit problem
  • Reinforcement learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Applied Mathematics

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