Modeling Hydra Behavior Using Methods Founded in Behavior-Based Robotics

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32 Chapter 7. Results and discussionTable 7.1: EA properties and settings for the best evolved RNN-based habituation unit. For allthree mutation cases, if a mutation occurs, one out of two mutation operators is selected. If, e.g., aconnection mutation occurs, there is a 50% chance of a connection being added (m 3 ), and a 50%chance of a connection being removed (m 4 ).Population size 50Initial RNN size 2CrossoverNot usedTournament size 5p tour 0.65p mut1,2 0.10p mut3,4 0.15p mut5,6 0.10Creep rate, c 2Range, weights and biases [−5, 5]Range, time constants [s] [10, 2000]⎛0.63 −5.00 5.00w = ⎝ −0.39 3.17 −5.002.30 −5.00 3.51w I = ( 0.51 0.00 3.71 )b = ( −0.75 −1.17 −3.72 )⎞⎠τ = ( 1107.21 95.50 509.15 ) (7.6)
7.1. Generation of behaviors 33................... ..❘✓✏❯...3✒✑... ✒.❦ .✣.... ..... .... . . . ..... ........❅ ✓✏... ❅❘❫ .....1✒✑. .✻ ... ✻.... ... . . . . .. ............. . . .. .............✓✏ ❄2.✒✑...✣❅■ .... .. ❅.. .. . . . .✲Figure 7.2: Evolved RNN habituation model.Response strength from 3 unit RNN10.9Training data, ISI = 8Output from RNN at ISI = 8Output from RNN at ISI = 4Output from RNN at ISI = 160.8Response strength0.70.60.50.40.30.20.10.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Time [s]x 10 4Figure 7.3: Response strength for best evolved RNN-based habituation model. ISIs are presentedhere in time-step units (1 time-step equals 2s, as described above). While the RNN performs wellon training data (ISI = 8), note that the recovery from habituation is not adequate for stimulus ofISI = 4.
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Page 11 and 12: Table of Contents1 Introduction 11.
Page 13: Table of ContentsviiB Recurrent neu
Page 16 and 17: 2 Chapter 1. Introductione.g. in [1
Page 18 and 19: 4 Chapter 1. Introduction1.3 Outlin
Page 20 and 21: 6 Chapter 2. NotationUpper-case let
Page 22 and 23: 8 Chapter 3. Animal behaviorPsychol
Page 24 and 25: 10 Chapter 3. Animal behaviorperiod
Page 27 and 28: Chapter 4Behavior-based roboticsAs
Page 29 and 30: Chapter 5HydraThis chapter presents
Page 31 and 32: 5.1. Hydra as a model organism 17ce
Page 33 and 34: Chapter 6Models and methodsIn this
Page 35 and 36: 6.1. The simulated system 21Contrac
Page 37 and 38: 6.1. The simulated system 23Table 6
Page 39 and 40: 6.3. Constituent behaviors 25Releas
Page 41 and 42: Chapter 7Results and discussionIn t
Page 43 and 44: 7.1. Generation of behaviors 29Expe
Page 45: 7.1. Generation of behaviors 31wher
Page 49 and 50: 7.1. Generation of behaviors 35Tabl
Page 51 and 52: 7.1. Generation of behaviors 374504
Page 53 and 54: 7.1. Generation of behaviors 39Tran
Page 55 and 56: 7.2. Generation of behavioral organ
Page 57: 7.2. Generation of behavioral organ
Page 60 and 61: 46 Chapter 8. Summary and conclusio
Page 62 and 63: 48 Bibliography[11] BÄCK, T., FOGE
Page 64 and 65: 50 Bibliography[41] MITCHELL, M., A
Page 66 and 67: 52 Bibliography[69] WATSON, J., Beh
Page 68 and 69: 54 Appendix A. Evolutionary algorit
Page 71 and 72: Appendix BRecurrent neural networks
Page 73: Appendix B. Recurrent neural networ

Modeling Hydra Behavior Using Methods Founded in Behavior-Based Robotics

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