Inhibition of convergence of mimicry rings by low learning abilities of predators

IF 0.8 Q4 ROBOTICS Artificial Life and Robotics Pub Date : 2024-07-20 DOI:10.1007/s10015-024-00956-5
Takashi Sato, Haruto Takaesu
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Abstract

Since Müllerian mimicry is more effective, the greater the number of species involved, multiple mimicry rings tend to gradually converge into one large mimicry ring, which then tends to expand. However, in nature, mimicry rings often do not converge into a single ring. It is believed that various factors lead to the diversification of mimicry rings. In this study, we conducted an evolutionary simulation experiment using a multi-agent system (MAS) based on the hypothesis that “predators with low learning ability cannot learn the patterns of toxic prey and continue to prey on species that exhibit Müllerian mimicry." Our aim was to investigate the inhibitory factor of convergence of mimicry rings. We use two types of agent models that make up the MAS: PREY-agent and PREDATOR-agent. Each PREDATOR-agent encounters a PREY-agent randomly at each step, decides whether to prey on the PREY-agent or not based on the pattern of the PREY-agent using its own feed-forward neural network (FFNN); it also uses its FFNN to learn the relationship between the PREY-agent’s pattern and the presence or absence of venom. The PREY-agent determines its own fitness based on the results of whether or not it was preyed upon by PREDATOR-agent, performs genetic evolution based on this fitness, and decodes its own genes to generate patterns. This pattern is generated using a modified L-system. Evolutionary simulation experiments using the MAS showed that the convergence of the mimicry ring is inhibited when the number of neurons in the hidden layer in the PREDATOR-agent’s FFNN is small, i.e., when the learning ability of the PREDATOR-agent is low.

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捕食者的低学习能力抑制拟态环的聚合
由于缪勒拟态更为有效,涉及的物种数量越多,多个拟态环往往会逐渐汇聚成一个大的拟态环,然后趋于扩大。然而,在自然界中,拟态环往往不会汇聚成一个单一的环。人们认为,多种因素导致了拟态环的多样化。在本研究中,我们根据 "学习能力低的捕食者无法学习有毒猎物的模式,并继续捕食表现出穆勒拟态的物种 "这一假设,利用多代理系统(MAS)进行了进化模拟实验。我们的目的是研究拟态环收敛的抑制因素。我们使用两种代理模型组成 MAS:猎物代理和猎手代理。每个 "捕食者 "代理在每一步都会随机遇到一个 "猎物 "代理,并利用自己的前馈神经网络(FFNN)根据 "猎物 "代理的模式决定是否捕食该 "猎物 "代理;它还利用自己的前馈神经网络学习 "猎物 "代理的模式与是否存在毒液之间的关系。PREY-agent 根据是否被 PREDATOR-agent 捕食的结果确定自己的适应性,并根据这种适应性进行基因进化,解码自己的基因以生成模式。这种模式是利用改进的 L 系统生成的。使用 MAS 进行的进化模拟实验表明,当 PREDATOR-agent 的 FFNN 隐藏层神经元数量较少时,即 PREDATOR-agent 的学习能力较低时,模仿环的收敛会受到抑制。
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来源期刊
CiteScore
2.00
自引率
22.20%
发文量
101
期刊介绍: Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics
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