Developing a Visualization Tool for Spider WebBuilding Algorithms

Developing a Visualization Tool for Spider Web-Building Algorithms 模擬蜘蛛結網之演算法設計及視覺化 具 開發 指導教授：尹邦嚴 陳怡孜 陳瑩哲 沈扇綸 郭怡君 11/1/2020 OPlab, IM, NTU 1

Outline I. Introduction l l II. Abstract Motivation Literature review l l Artificial Intelligence Related spider papers III. Spider web-building algorithm design IV. Visualization tool prototype of the model l l V. 11/1/2020 Internal factors External factors Conclusion and future work OPlab, IM, NTU 2

I. Introduction 1/2 l Abstract – Sequential behavior of spider web-building model – Internal factors of the spider web-building model – External factors of the spider web-building model – Implementation of visualization tool 11/1/2020 OPlab, IM, NTU 3

I. Introduction 2/2 l Motivation Figure 1. The motivation of the model 11/1/2020 OPlab, IM, NTU 4

Outline I. Introduction l l II. Abstract Motivation Literature review l l Artificial Intelligence Related spider papers III. Spider web-building algorithm design IV. Visualization tool prototype of the model l l V. 11/1/2020 Internal factors External factors Conclusion and future work OPlab, IM, NTU 5

II. Literature review　1/3 l Artificial Intelligence – Swarm intelligence • Geese migratine –fly in a “V” formation • Bees and wag-tail dance • Ant colony optimization (ACO) Solve discrete optimization problems (Dorigo, 1992) • Particle swarm optimization (PSO) A optimization for representing sociocognition of human and artificial agents tool (Kennedy and Eberhart, 1995) 11/1/2020 OPlab, IM, NTU 6

II. Literature review 2/3 l Related spider papers – – 11/1/2020 Influence of the genes on the web (Kirnk, 1996) Wind, Temperature, and Humidity (Vollrath, 1997) Prey size (Vollrath, 1998) The pattern of the capture spiral(李蔡彥and林翰儂, 2004) OPlab, IM, NTU 7

II. Literature review 3/3 l Related spider artificial intelligence – The pattern of the capture spiral ( 李蔡彥and林翰儂, 2004) Figure. 2 Capture spiral pattern Figure. 3 Different capture spiral pattern 11/1/2020 OPlab, IM, NTU 8

Outline I. Introduction l l II. Abstract Motivation Literature review l l Artificial Intelligence Related spider papers III. Spider web-building algorithm design IV. Visualization tool prototype of the model l l V. 11/1/2020 Internal factors External factors Conclusion and future work OPlab, IM, NTU 9

III. Spider web-building algorithm model design 1/8 Figure. 4 The factor-analysis tree of web-building. 11/1/2020 OPlab, IM, NTU 10

III. Spider web-building algorithm model design 2/8 Figure. 5 The rule-analysis tree of web-building. Auxiliary spiral (AS) Capture spiral (CS) Figure. 6 The real spider web. 11/1/2020 OPlab, IM, NTU Influence of the genes on the web (Kirnk, 1996) 11

III. Spider web-building algorithm model design 3/8 • Radii Gene 1： Control the number of radii (　 ) and base angle (　). Gene 2：Control the difference of the angle (　) from the north to the first radii. Gene 3：Control the difference of the angle (　) between the other radii. 11/1/2020 OPlab, IM, NTU Figure. 7 The relation between radii and gene 12

III. Spider web-building algorithm model design 4/8 • Frame The distance (HF) between hub and frame is the length of the radii (RL) subtract the length which is control by Gene 4 (　). HF = RL Because of the effects of the gravity, the distance between hub and downward frame is longer than the others distance. 11/1/2020 OPlab, IM, NTU RL HF Figure. 8 The relation between frame and gene 13

III. Spider web-building algorithm model design 5/8 • AS The effects of the interactions between Gene 5 (　) and Gene 6 ( 　 　 ) control the intersection points of the AS and radii, resulting in a clockwise spiral (AS). Figure. 9 The relation between AS and gene 11/1/2020 OPlab, IM, NTU 14

III. Spider web-building algorithm model design 6/8 • CS Along the AS, a spider creates an anticlockwise spiral (CS). The intersection points of the CS and radii are effected by Gene 7 (　　 ). Figure. 10 The relation between CS and gene 11/1/2020 OPlab, IM, NTU 15

III. Spider web-building algorithm model design 7/8 • CS reverse Using reverse to fill the remaining space of web, which is uncovered with CS. Figure. 11 The relation between radii and gene 11/1/2020 OPlab, IM, NTU 16

III. Spider web-building algorithm model design 8/8 l. Experiments Figure. 12 Sequential behavior of spider web-building. 11/1/2020 OPlab, IM, NTU 17

Outline I. Introduction l l II. Abstract Motivation Literature review l l Artificial Intelligence Related spider papers III. Spider web-building algorithm design IV. Visualization tool prototype of the model l l V. 11/1/2020 Internal factors External factors Conclusion and future work OPlab, IM, NTU 18

IV. Visualization tool prototype of the model 1/23 l Internal factors – – Explanation of the rules Experiments l l l Model simulation Verification External factors – – Explanation of the rules Experiments l l 11/1/2020 Model simulation Verification OPlab, IM, NTU 19

IV. Spider web-building algorithm model design 2/12 l. Internal factors Figure. 6 The internal factor-analysis tree of web-building 11/1/2020 OPlab, IM, NTU 20

IV. Visualization tool prototype of the model 3/23 l Internal factors – Body size (χ) Larger spiders would build larger webs – Weight (ψ) Heavier spiders with the same size of their body would build larger webs – Gland silk (ω) Larger gland silk with the same size of their body build larger webs – χ, ψ, ω interaction 11/1/2020 OPlab, IM, NTU 21

IV. Visualization tool prototype of the model 3/23 l Body size (χ) – experiments l Model simulation l Verification Figure. 13(a) minimize χ, ψ, ω 11/1/2020 Figure. 13(b) Maximize χ, OPlab, IM, NTU minimize ψ, ω 22

IV. Visualization tool prototype of the model 4/23 l Weight (ψ) – experiments l Model simulation l Verification Figure. 14(a) minimize χ, ψ, ω 11/1/2020 Figure. 14(b) Maximize ψ, OPlab, IM, NTU minimize χ, ω 23

IV. Visualization tool prototype of the model 5/23 l Gland silk (ω) – experiments l Model simulation l Verification Figure. 15(a) minimize χ, ψ, ω Figure. 15(b) Maximize ω, 11/1/2020 OPlab, IM, NTU minimize χ, ψ 24

IV. Visualization tool prototype of the model 6/23 l χ, ψ, ω interaction experiments – experiments l Model simulation l Verification Figure. 16(a) minimize χ, ψ, ω 11/1/2020 Figure. 16(b) Maximize ω, χ, ψ OPlab, IM, NTU 25

IV. Visualization tool prototype of the model 1/23 l Internal factors – – Explanation of the rules Experiments l l l Model simulation Verification External factors – – Explanation of the rules Experiments l l 11/1/2020 Model simulation Verification OPlab, IM, NTU 26

IV. Visualization tool prototype of the model 7/23 l. External factors –Climate –Surrounding environments –Prey size Figure. 17 The external factor analysis tree of web-building 11/1/2020 OPlab, IM, NTU 27

IV. Visualization tool prototype of the model 8/23 l Climate – Wind Ø Affect mesh Ø Affect the length and the number of radius – Temperature Ø Affect mesh Ø Affect the frequency of CS reverse – Humidity Ø Affect mesh Ø Affect the viscosity of CS 11/1/2020 OPlab, IM, NTU 28

IV. Visualization tool prototype of the model 9/23 l Wind( ) – experiments • Model simulation Adjusting the control value of Gene to create new mesh. Figure. 18 Wind influence in mesh 11/1/2020 OPlab, IM, NTU 29

IV. Visualization tool prototype of the model 10/23 • Verification-wind Figure. 19(a) Wind effect mesh 　： 0 m/s ： 45% 11/1/2020 ： 25℃ Bias: 16. 33% Figure. 19(b) Wind effect mesh 　 ： 1 m/s 　 ： 45% OPlab, IM, NTU ： 25 ℃ Bias: 17. 33% 30

IV. Visualization tool prototype of the model 11/23 l Temperature( ) – experiments • Model simulation Adjusting the control value of Gene to create new mesh. Figure. 20 Temperature influence in mesh 11/1/2020 OPlab, IM, NTU 31

IV. Visualization tool prototype of the model 12/23 • Verification-temperature Figure. 21(a) Temperature effect mesh Figure. 21(b) Temperature effect mesh 　： 24 ℃ ： 55% 11/1/2020 ： 0 m/s Bias: 0. 60% 　 OPlab, IM, NTU ： 12 ℃ 　 ： 55% ： 0 m/s Bias: 1. 67% 32

IV. Visualization tool prototype of the model 13/23 l Humidity( ) – experiments • Model simulation Adjusting the control value of Gene to create new mesh. Figure. 22 Humidity influence in mesh 11/1/2020 OPlab, IM, NTU 33

IV. Visualization tool prototype of the model 14/23 • Verification-humidity Figure. 23(a) Humidity effect mesh 　 ： 70% ： 24 ℃ 11/1/2020 Figure. 23(b) Humidity effect mesh ： 0 m/s Bias: 0. 87% OPlab, IM, NTU ： 20% ： 24 ℃ ： 0 m/s Bias: 0. 16% 34

IV. Visualization tool prototype of the model 15/23 l Surrounding environments – To affect the area of the web – The goal of web-building is to maximize 11/1/2020 OPlab, IM, NTU 35

IV. Visualization tool prototype of the model 16/23 l Surrounding environments – experiments l Model simulation l Verification Figure. 24(a) Available space relatively small 11/1/2020 Figure. 24(a) Available space relatively big OPlab, IM, NTU 36

IV. Visualization tool prototype of the model 17/23 l Prey size – Affect mesh – Applying the past experiences for catching difference sizes of prey to adjust the mesh of the next web-building 11/1/2020 OPlab, IM, NTU 37

IV. Visualization tool prototype of the model 18/23 l Prey size – 11/1/2020 experiments l Model simulation The major concept is that using animals learning to simulate the real world l Verification OPlab, IM, NTU 38

IV. Visualization tool prototype of the model 19/23 • Verification Figure. 25(a) The first day 11/1/2020 Figure. 25(b) The second day OPlab, IM, NTU 39

IV. Visualization tool prototype of the model 20/23 • Verification Figure. 25(c) The third day 11/1/2020 Figure. 25(d) The fourth day OPlab, IM, NTU 40

IV. Visualization tool prototype of the model 21/23 • Summary 11/1/2020 OPlab, IM, NTU 41

Outline I. Introduction l l II. Abstract Motivation Literature review l l Artificial Intelligence Related spider papers III. Spider web-building algorithm design IV. Visualization tool prototype of the model l l V. 11/1/2020 Internal factors External factors Conclusion and future work OPlab, IM, NTU 42

V. Conclusion and future work 1/2 l Success in simulation the sequential behavior of spider web-building l The proposed model was verified by comparing with numerical experiment of biologists l Unobserved behavior of spiders could be successfully predicted 11/1/2020 OPlab, IM, NTU 43

V. Conclusion and future work 2/2 l Long term learning l Simulating the behavior of spider to repair the web l Designing social spider web-building model l Applying spider web-building algorithms to optimization problems 11/1/2020 OPlab, IM, NTU 44

Acknowledgements l. We thank our adviser Ph. D Peng-Yeng Yin. l. We thank NSC to provide our budget. Project name: 模擬蜘蛛結網之演算法設計及視覺化 具開發 Project number: 94 -2815 -C-260 -014 -E Adviser : 尹邦嚴 Name: 陳怡孜 11/1/2020 OPlab, IM, NTU 45

Q＆A ? 11/1/2020 OPlab, IM, NTU 46

Thank you !! 11/1/2020 OPlab, IM, NTU 47