TY - GEN

T1 - Tug-of-war model for multi-armed bandit problem

AU - Kim, Song Ju

AU - Aono, Masashi

AU - Hara, Masahiko

PY - 2010

Y1 - 2010

N2 - 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. Our model can efficiently manage this "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-greedy algorithm and modified softmax algorithm.

AB - 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. Our model can efficiently manage this "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-greedy algorithm and modified softmax algorithm.

KW - Amoeba-based computing

KW - Bio-inspired computation

KW - Multi-armed bandit problem

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=79956331440&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79956331440&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-13523-1_10

DO - 10.1007/978-3-642-13523-1_10

M3 - Conference contribution

AN - SCOPUS:79956331440

SN - 3642135226

SN - 9783642135224

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 69

EP - 80

BT - Unconventional Computation - 9th International Conference, UC 2010, Proceedings

T2 - 9th International Conference on Unconventional Computation, UC 2010

Y2 - 21 June 2010 through 25 June 2010

ER -