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The difference between male and female robots is that after they mate, the woman is unproductive for a set period, while the male can mate again. Therefore, males have a greater variance in reproductive success because they are actively looking for a scarce resource. Also, reproductive females are less active compared to males. They wait for males to find them. On the other hand, non-productive females are as active as males when it comes to looking for food. They only look for food and are not interested in anything else. The final difference was in the preference of type of food and offspring care. Only if males do not have parental certainty.
What is the objective of this study?
Mating has different consequences for male and female. The objective of this study is to describe the behavioural consequences of the single difference that males can reproduce after mating while females cannot. The idea behind this is to reproduce this phenomenon in artificial systems. If the system will behave the same as the real phenomenon, the model that was used can help explain the phenomenon. The robots used in the experiment are not created by the researchers, but they are male and female robots living and reproducing in an artificial environment. This will teach the differences in evolved behaviour between male and female robots. Robots can be useful to illustrate the difference between man and woman because when constructing robots, the behavioural assumptions need to be stated specifically.
One of the differences is that reproductive female robots are less active than male robots. However, when females mate successfully this is not the case. The stereotype of active males versus reactive females must be guarded against. It should be objectively measured. The robots that are used in this case are highly simplified, and the results of the study may apply to some species, but not to others. The nature of sexual differences in species depends on the adaptive pattern of certain species. The robots used in this experiment provide a better understanding in observed behaviour in real animals plus their evolutionary origin.
Not many experiments have been done on sexual differences in robots and computational models on the behaviour of male and female organisms. Studies that try to investigate genetic algorithms are done by recombining genotypes of two different animals. The only difference between the male and female robots in this experiment is the consequence of mating. It’s assumed the species are sexually different, without specifying the ways in which they are. The only dissimilarity is appearance. The goal is to find out if male and female robots behave differently and what these differences are.
How is the experiment executed?
A group of hundred Khepera robots was used in this study. They all have an energy level ranging from zero to one, and when the energy level is zero, the robot disappears. Food tokens are used to represent eating. Half of the robots in the experiment are male, the other half is female. Mating happens when two robots of a different sex touch. In the end, the number of mating experiences is counted, and the top ten males and top ten females generate offspring. This represents the evolution and is repeated 10 times for 1,000 generations. The behaviour of the robots is controlled by a neural network encoded into the genes of that robot.
What were the results of the experiment?
The variance in reproductive success is the same for males and females. However, the male robots have a higher number of mating events. There was an increase in number of mating events, so the robots evolve in the ability to successfully mate and eat. Length of life and number of food tokens is correlated, but not perfectly. It seems that females can eat more efficiently. Length of life and mating success was correlated differently for males and females; female mating success is strongly correlated with length of life, which is not true for males. This implies that there are two adaptive strategies used.
Males and females produce different results in terms of mating and eating. Females behave differently depending whether they are reproductive or nonreproductive. Males were always active in looking for food or reproductive females, whereas only nonreproductive females were active. Reproductive females were not active and were waiting for males. Nonreproductive females were active, but were only looking for food, not for males.
The behaviour of the robots in the experimental laboratory was studied by a controlled experiment. The robot is given the choice between two items. The items can be male, reproductive female, nonreproductive female or a food token. Males prefer the reproductive female over food and even stronger over a nonreproductive female. When the male must choose between a nonreproductive female and food, it prefers food. Reproductive females prefer food over a male, also when she needs to choose between a male and a nonreproductive female, the choice of male is just a little more preferable than the nonreproductive female.
Finally, the choice between different types of food was simulated by colouring the available food. Males do not prefer one type of food over another. Females have specific food preferences dependent on their environment. Reproductive females prefer more energetic food. When female robots are present, the male tents to eat less (energetic) food. This can be explained by males being socially aware of their environment and the presence of females. They will behave accordingly.
What is suggested for further research?
In the article, five main suggestions are made to elaborate on the current research.
The emergence of families could be studied. An important implication is that the life of the robots should then be linked to specific places. They should be able to recognize the location of these places.
The robots are described as male and female but are not individually different. The influence of sexual attractiveness could be measured to mirror sexual selection. Things that could be considered are recreational sex, which is mating between a male and a nonreproductive female. Also, it could be that sexual attractiveness is different for nonreproductive versus reproductive females.
Motivations for animals are different and rely on emotional states and their expression. This is because not more than one motivation can be pursued at one time. Emotional states help guide what decisions are made and are based on the interaction of brain with the rest of the body. However, the expression of emotions that are related to sex and parenting might be a whole other subject for research.
A limitation of the current study is that stereotypes need to be more realistic. One example is that male robots only inherit genotypes of their father whereas females inherit the genotype of their mother. There is no crossover.
Lastly, female menopause and grandmothers could be considered. In real life, females become permanently nonreproductive. Female robots in menopause could be used to explore the grandmother hypothesis: that females continue to live after menopause to help their daughters take care of their offspring.
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Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
Study Guide with articlesummaries for Artificial Intelligence at Leiden University
Articlesummaries with Artificial Intelligence at Leiden University
Table of content
- Modeling visual recognition from neurobiological constraints
- Achieving machine realization of truly human-like intelligence
- Untangling invariant object recognition
- Computing machinery and intelligence
- Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project
- Robots with instincts
- Male and female robots
- Speed of processing in the human visual system
- Sparse but not ""Grandmother-cell"" coding in the medial temporal lobe
- Perceptrons
- Learning and neural plasticity in visual object recognition
- Breaking position-invariant object recognition
- A feedforward architecture accounts for rapid categorization
- Hierarchical models of object recognition in cortex
- Articlesummaries with prescribed articles for AI - 2020/2021
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