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Showing 1,701 - 1,720 results of 4,823 for search '(( algorithm within function ) OR ((( algorithm python function ) OR ( algorithm a function ))))', query time: 0.47s Refine Results
  1. 1701
    Grid world Program 2.txt (R script).

    Grid world Program 2.txt (R script). by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  2. 1702
    Sensory–Motor Program.nlogo (NetLogo program).

    Sensory–Motor Program.nlogo (NetLogo program). by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  3. 1703
    NetLogo world movie.

    NetLogo world movie. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  4. 1704
    Nash equilibria Si of non-Sensory motor IBM.

    Nash equilibria Si of non-Sensory motor IBM. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  5. 1705
    Measurements of simulations.

    Measurements of simulations. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  6. 1706
    Payoff matrices of grid world model.

    Payoff matrices of grid world model. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  7. 1707
    Nash equilibria Si of grid world model.

    Nash equilibria Si of grid world model. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  8. 1708
    Nash equilibria Si of sensor–motor IBM.

    Nash equilibria Si of sensor–motor IBM. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  9. 1709
    Nash equilibrium conditions of sensory–motor IBM.

    Nash equilibrium conditions of sensory–motor IBM. by Hiroyuki Ichijo (13891153)

    Published 2025
    “…Moreover, Nash equilibrium behaviors were also identified under a simpler non-sensory motor algorithm. Despite the similarity, the non-sensory motor algorithm and the sensory–motor algorithm had distinct adaptive significance. …”
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  10. 1710
    Partial behavioral switching.

    Partial behavioral switching. by Hiroyuki Ichijo (13891153)

    Published 2025
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  11. 1711
    GMM-KVS method flowchart.

    GMM-KVS method flowchart. by Jingnan Yan (20727331)

    Published 2025
    “…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. …”
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  12. 1712
    Collected demonstration trajectories.

    Collected demonstration trajectories. by Jingnan Yan (20727331)

    Published 2025
    “…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. …”
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  13. 1713
    Schematic diagram of the elbow method.

    Schematic diagram of the elbow method. by Jingnan Yan (20727331)

    Published 2025
    “…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. …”
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  14. 1714
    Effects of prey’s detection distance (<i>D</i><sub><i>s</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<sub><i>i</i></sub>), with agents adopting Nash equilibrium behaviors under the sensory–motor algorithm across all conditions (<i>c</i><sub><i>b</i></sub> = 0, 0.001, 0.02, 0.04, and <i>r</i> = 3.2, 3.6, 4.0).

    Effects of prey’s detection distance (<i>D</i><sub><i>s</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<su... by Hiroyuki Ichijo (13891153)

    Published 2025
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  15. 1715
    Effects of prey’s speed change probability (<i>K</i><sub><i>s</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<sub><i>i</i></sub>) under the non-sensory motor algorithm across all conditions (<i>c</i><sub><i>b</i></sub> = 0, 0.001, 0.02, 0.04, and <i>r</i> = 3.2, 3.6, 4.0).

    Effects of prey’s speed change probability (<i>K</i><sub><i>s</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoff... by Hiroyuki Ichijo (13891153)

    Published 2025
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  16. 1716
    Payoff spaces under <i>D</i><sub><i>i</i></sub> combinations where behavioral switching occurred and under all other <i>D</i><sub><i>i</i></sub> combinations of non-behavioral switching.

    Payoff spaces under <i>D</i><sub><i>i</i></sub> combinations where behavioral switching occurred and under all other <i>D</i><sub><i>i</i></sub> combinations of non-behavioral swit... by Hiroyuki Ichijo (13891153)

    Published 2025
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  17. 1717
    Landscapes of payoff differences (<i>P</i><sub><i>diff</i></sub>) and Nash equilibrium speed changes (<sub><i>i</i></sub>) with the sensory–motor algorithm in the absence of the behavioral cost coefficient (<i>c</i><sub><i>b</i></sub> = 0).

    Landscapes of payoff differences (<i>P</i><sub><i>diff</i></sub>) and Nash equilibrium speed changes (<sub><i>i</i></sub>) with the sensory–motor algorithm in the absence of the be... by Hiroyuki Ichijo (13891153)

    Published 2025
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  18. 1718
    Effects of predators’ speed change probability (<i>K</i><sub><i>w</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<sub><i>i</i></sub>) under the non-sensory motor algorithm across all conditions (<i>c</i><sub><i>b</i></sub> = 0, 0.001, 0.02, 0.04, and <i>r</i> = 3.2, 3.6, 4.0).

    Effects of predators’ speed change probability (<i>K</i><sub><i>w</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium pa... by Hiroyuki Ichijo (13891153)

    Published 2025
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  19. 1719
    Effects of predators’ detection distance (<i>D</i><sub><i>w</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<sub><i>i</i></sub>) with agents adopting Nash equilibrium behaviors under the sensory–motor algorithm across all conditions (<i>c</i><sub><i>b</i></sub> = 0, 0.001, 0.02, 0.04, and <i>r</i> = 3.2, 3.6, 4.0).

    Effects of predators’ detection distance (<i>D</i><sub><i>w</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs... by Hiroyuki Ichijo (13891153)

    Published 2025
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  20. 1720
    Landscapes of payoff differences (<i>P</i><sub><i>diff</i></sub>) and Nash equilibrium speed changes (<sub><i>i</i></sub>) with the sensory–motor algorithm under the small behavioral cost coefficient (<i>c</i><sub><i>b</i></sub> = 0.001).

    Landscapes of payoff differences (<i>P</i><sub><i>diff</i></sub>) and Nash equilibrium speed changes (<sub><i>i</i></sub>) with the sensory–motor algorithm under the small behavior... by Hiroyuki Ichijo (13891153)

    Published 2025
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