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Evolution of Altruistic Robots


Abstract and Figures

In this document we examine the evolutionary methods that may lead to the emergence of altruistic cooperation in robot collectives. We present four evolutionary algorithms that derive from biological theories on the evolution of altruism in nature and compare them systematically in two experimental scenarios where altruistic cooperation can lead to a performance increment. We discuss the relative merits and drawbacks of the four methods and provide recommendations for the choice of the most suitable method for evolving altruistic robots.
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Evolution of Altruistic Robots
Dario Floreano1, Sara Mitri1, Andres Perez-Uribe2, Laurent Keller3
1Laboratory of Intelligent Systems, EPFL, Lausanne, Switzerland
2University of Applied Sciences, Yverdon, Switzerland
3Department of Ecology and Evolution, University of Lausanne, Switzerland
Abstract. In this document we examine the evolutionary methods that
may lead to the emergence of altruistic cooperation in robot collectives.
We present four evolutionary algorithms that derive from biological the-
ories on the evolution of altruism in nature and compare them systemati-
cally in two experimental scenarios where altruistic cooperation can lead
to a performance increment. We discuss the relative merits and draw-
backs of the four methods and provide recommendations for the choice
of the most suitable method for evolving altruistic robots.
1 Altruistic Cooperation in Nature
The competition for survival and reproduction postulated by Darwin
seems at odds with the observation that some organisms display cooper-
ative behaviors. In order to understand the evolutionary conditions when
cooperation can emerge, Lehmann and Keller [14] suggested to distin-
guish between two types of cooperation (figure 1), namely the situations
where a cooperator does not pay a fitness cost from helping other indi-
viduals and the situations where a cooperator must pay a fitness cost
for helping other individuals. Let us remember that in biology fitness
benefits and costs translate into the number of genetic copies that an
individual can produce or loose with respect to its baseline reproduction
The situation where cooperation generates a fitness benefit without any
cost to the cooperator is relatively common in nature. This situation can
be further divided in two cases, when the benefit is immediate or direct
and when the benefit is indirect. Examples of cooperation with direct
benefits include nest building and group hunting. Whenever a cooperator
obtains an immediate and direct benefit from helping another individual,
cooperation will always evolve and remain stable, no matter whether the
receiving individuals belong to another species or have never been seen
If the benefit is indirect, i.e., the act of helping is not immediately recip-
rocated or the benefit appears only in the long term, cooperation evolves
only if individuals have an initial tendency to cooperate, interact to-
gether several times, and can both recognize the partner and remember
the outcome of previous interactions. If these conditions are satisfied,
cooperation will always evolve and remain stable even if cooperating
individual belong to different species.
Green beard effect
Genetic relatedness
No cost
Indirect reciprocation
No cost
Direct reciprocation
Fig. 1. Conditions for the evolution of cooperation according to the classification sug-
gested by Lehmann and Keller [14]. When there is no cost for the cooperator, cooper-
ation can evolve if there is direct reciprocation or indirect reciprocation (in the latter
case, a reputation system may help). When there is a cost for the cooperator, cooper-
ation can evolve if individuals have a high level of genetic relatedness or if they both
have greenbeard genes. The pattern indicates the genetic similarity between individ-
uals. The size change after cooperation indicates the cost or benefit of cooperation.
Figure loosely inspired by figure 5.9 in [29].
It has also been shown that recognition of other individuals and mem-
orization of the outcomes of the interactions is not necessary if there
is a reputation system that informs how cooperative an individual is
[21]. The way in which animals and people decide to cooperate has been
studied extensively in game theory, notably within the framework of the
Prisoner’s Dilemma game.
On the other hand, the situation where cooperation implies a fitness cost
for the cooperator is less common. Cooperation with a cost is also known
as altruism because the cooperator helps other individuals at its own ex-
pense. Parental care is an instance of altruism directed towards offspring
of the individual because it implies an energetic cost for the parent. The
specialization of ant colonies into large numbers of sterile workers (for
food collection, nest defense, rearing of the pupae of the queen, etc.) is
yet another instance of altruistic cooperation where the helping workers
incur the highest fitness cost because they cannot reproduce.
Building on earlier intuitions by Haldane [10], Hamilton [11] suggested
that altruism can evolve if the cooperator is genetically related to the
recipient of help. In this case, even if the cooperator cannot propagate its
own genes to the next generation, its altruistic act will increase the prob-
ability that a large portion of those genes will be propagated through the
reproduction of the recipient of the altruistic act. Hamilton [11] proposed
the notion of inclusive fitness, which is the sum of the individual fitness
and of the fitness effects caused by its own act on the portion of genes
shared with other individuals. The portion of shared genes between two
individuals is known as genetic relatedness. He [11] predicted that altru-
istic cooperation will evolve if the inclusive fitness of the helper is larger
than zero
rb c > 0 (1)
where ris the coefficient of genetic relatedness, bis the fitness benefit of
the recipient(s) of help, and cis the fitness cost of the helper. To use an
example suggested by Haldane, in the case of brothers, where r= 1/2,
an individual may be willing to sacrifice its own life and thus pay the
maximum cost c= 1 if its act increases more than twice b > 2 the fitness
of the brother. For cousins, where r= 1/8, an individual may be willing
to pay the maximum cost if its act increases the fitness of the cousin
more than eight times.
Hamilton’s inequality applies to average genetic relatedness over the en-
tire genotype and population, i.e. it is not restricted to the sharing of
a specific set of genes. It also applies to the case where the act of co-
operation benefits multiple individuals with various degrees of related-
ness. The theory of kin selection [16], which developed from Hamilton’s
model, predicts that the ratio of altruistic individuals in a population is
related to the degree of kinship, or genetic relatedness, among individu-
als. Although the theory is widely accepted, its quantitative validation in
nature has not yet been done because it is difficult to precisely measure
the values of the three variables in equation 1.
For evolution of altruism to occur, helping should be directed towards
related individuals. This is more likely to happen when individuals share
the same geographical space, such as a nest, for social activities. Indeed,
most cases of altruistic cooperation are found in families of social insects
[12]. Kin selection does not require that individuals recognize kin indi-
viduals or know their degree of genetic relatedness. As long as the act of
altruism preferentially benefits genetically-related individuals, altruism
will spread throughout the population and remain stable.
A particular case of altruism occurs when individuals share few specific
genes that favor cooperating behaviors only between individuals having a
specific phenotypic character, such as a green beard [7], and that express
the same phenotypic character. However, altruism due to greenbeard ef-
fects can be disrupted if the linkage between the genes responsible for the
green beard and the genes responsible for altruistic behavior is disrupted.
For example, a mutant individual with a green beard but without the
altruistic behavior will have larger inclusive fitness than individuals who
have both types of genes; consequently, it will spread in the population
and destroy altruistic cooperation [14].
The four conditions for the evolution of cooperation, direct or indirect
reciprocity, genetic relatedness and greenbeard genes, which can all be
included within a single model [14], hold only if cooperation brings a net
fitness advantage to the individuals. In some societies, the actual values
of benefits and costs are distorted by means of coercion and punishment
to ensure maintenance of cooperative behavior.
Yet another explanation for the evolution of altruistic cooperation is
provided by the theory of levels of selection, which argues that altruistic
cooperation may also evolve in colonies of genetically unrelated individu-
als that are selected and reproduced all together at a higher rate than the
single individuals composing the colony [31]. This could happen in situ-
ations where the synergetic effect of cooperation by different individuals
provides a higher fitness to the group with respect to other competing
However, the colony-level selection has been criticized because genetic
mutations at the level of the individual are more likely and frequent
than mutations at the level of the colony, thus creating stronger compe-
tition among individuals than among colonies. It has also been argued
that the transition from uni-cellular to multi-cellular organisms can be
explained by kin selection because all cells share the same genotype [30].
Although proponents of colony-level selection respond to these criticisms
by pointing to evidence for the evolution of colony-level features that de-
crease individual conflict (such as a reduced mutation rate of individual
organisms or cells that compose the colony), the theory of colony-level
selection is still widely debated. Furthermore, colony-level selection may
eventually lead to high genetic relatedness, thus making the disambigua-
tion between the original driving forces that led to altruistic cooperation
even more difficult.
2 Artificial Evolution of Cooperation
In robotics, the evolution of collective behaviors has been studied in
several experiments, but often without attention to whether it involves
only behavior coordination or also cooperation and whether cooperation
involves a cost for the individuals. In those situations where cooperation
is explicitly mentioned, it is described as a situation where robots obtain
an advantage by working together rather than working in isolation.
When it comes to evolving teams of robots, the experimenter is presented
with two design choices: 1) whether robots should be genetically identical
or different; and 2) whether the fitness used for selection should take
into account the performance of the entire group or only that of single
individuals. These two choices are analogous to the issues of genetic
relatedness and of level of selection that were discussed above in the
context of the biological literature. If we consider only the extreme cases
of each design choice, robots in a team can be genetically homogeneous
(clones) or heterogeneous (they differ from each other); and the fitness
can be computed at the level of the team (in which case, the entire team
of individuals is reproduced) or at the level of the individual (in which
case, only individuals of the team are selected for reproduction).
Biological theory tells us that the evolution of genetically related robots
should lead to cooperative behaviors, but the question of the appropriate
level of selection, or fitness computation, is still open for discussion. Fur-
thermore, biological theory does not make any prediction on the com-
parative performances that we may expect from robots evolved under
different conditions.
The majority of current approaches to the evolution of multi-agent sys-
tems use genetically homogeneous teams evolved with team-level selec-
tion (a comparative survey can be found in [27]). Where the reasons
for the choice of genetically homogeneous teams are made explicit, it is
Fig. 2. A swarm-bot composed of four interconnected s-bots in chain formation.
argued that homogeneous teams are easy to use [3, 26], require fewer
evaluations [15, 25], scale more easily [6], and are more robust against
the failure of team members [6, 24] than heterogeneous teams.
The choice of level of selection is rarely discussed explicitly despite the
fact that fitness distribution leads to credit assignment problems [9, 19]
in many cooperative multi-agent tasks because individual contributions
to team performance are often difficult to estimate or difficult to monitor
Let us consider the case of evolving control systems for a population
of identical robots, the s-bots shown in figure 2, which can self-connect
to form a swarm-bot [20]. In a simple case, a swarm-bot of four s-bots
assembled in chain formation were evolved for the ability to move coordi-
nately on a flat terrain. Each s-bot was provided with a neural controller
where sensory neurons were directly connected to the motors neurons
that controlled the desired speed of the tracks. The sensory neurons re-
ceived information from distance sensors around the body of the robot
and from a torque sensor that measured the amount of torsional force
exerted by other robots. In this case, all s-bots in the swarm-bot were
genetically identical and the fitness measured the progress of the entire
swarm-bot on the ground. Evolved controllers were also capable of pro-
ducing coordinated movement also when the swarm-bot was augmented
by additional s-bots and re-organized in different shapes. Swarm-bots
also dynamically rearranged their shape so as to effectively negotiate
narrow passages and were capable of moving on rough terrains over holes
or slopes that could not be passed by a single robot. Such robots also
collectively avoided obstacles and coordinated to transport heavy objects
[1, 2, 26].
The choice of team-level selection in this case was imposed by the dif-
ficulty to assign fitness values to individual s-bots that composed the
swarm-bot. However, the choice of genetically related teams was not
best teams
best teams
Select best
Select best
Level of Selection
Team composition
Fig. 3. Four conditions for the evolution of robot collectives. A population (large oval)
is composed of several teams (medium ovals), each of which is composed of several
robots (small circles). Genetic team composition is varied by either composing teams of
robots with identical genomes (homogeneous, identical shading), or different genomes
(heterogeneous, different shading). The level of selection is varied by either measur-
ing team performance and selecting teams (team-level selection) or measuring indi-
vidual performance and selecting individuals independently of their team affiliation
(individual-level selection).
duly justified because it may have prevented the emergence of special-
ized individuals.
The question therefore remains of what is the best performing set of
choices for tasks that benefit from cooperative behaviors when there is
both a choice between genetic relatedness and level of selection. In the
remainder of this chapter, we will describe the systematic comparison of
these design choices for two sets of experiments that can benefit from
the evolution of altruistic cooperation.
2.1 Evolutionary Conditions
We compared four evolutionary conditions (figure 3): genetically homoge-
neous teams evolved with team-level selection; genetically homogeneous
teams evolved with individual-level selection; genetically heterogeneous
teams evolved with team-level selection; and genetically heterogeneous
teams evolved with individual-level selection. Team-level selection (akin
to colony-level selection) consisted of computing the fitness of the team
and reproducing the robots in the best teams to create a new population
Fig. 4. A team of artificial ants is foraging for food tokens. Small food tokens can be
transported by a single ant and are consumed by that ant when it manages to get to
the nest. Large food tokens require the cooperation of two ants to be transported to
the nest, but they are shared by the entire team. However, the share of a large food
token provides less food intake to each individual than a small token. For the sake of
simplicity, in this figure we are only showing 10 artificial ants.
of robot teams. Individual-level selection instead consisted of computing
the fitness of individual robots (notice that even robots with identical
genomes can obtain different fitness because they are exposed to different
situations) and reproducing the best ones independently of their team
affiliation to recreate new teams.
The comparisons were carried out in situations where both selfish and
altruistic behaviors could produce fitness increments over generations,
but altruistic behavior corresponded to larger fitness increments, that is
to a larger quantity of work accomplished by the team of robots. In a
first set of experiments, we resorted to simplified behaviors and simulated
environments in order to disentangle fitness differences due to the effects
of the evolvability of control systems in situated environments from the
effects of the four evolutionary conditions. In a second set of experiments,
we resorted to neural controllers in real and simulated robots.
2.2 Altruistic Foraging
In the first set of experiments, we used an agent-based model of a team
of artificial ants performing a foraging task (figure 4). The agents or
artificial ants (e.g., robots) are supposed to look for food items randomly
scattered in a foraging area. There are two kinds of food items, small food
items which can be transported by single agents to the nest, and large
food items, which can only be transported if two ants cooperate. When
a cooperative foraging ant happens to find a large food item, it sends a
local message asking for help. Given the local nature of the help message,
another cooperative individual will only be able to help the first one if
it happens to be close to it and hear its message. For sake of simplicity
large food items can only be transported by a pair of ants and we have
not included a pheromone-like communication among ants.
Each ant is endowed with a set of three genes encoding three threshold
values that are used to determine if one or more predefined behaviors
(b0,b1or b2) are activated at each step of a foraging trial, as shown in
the table.
b0b1b2Behavioral strategies
0 0 0 do nothing
1 0 0 if a small food item is found, bring it to the nest, ignore
large food items, and do not help other ants
0 1 0 if a large food item is found, stay and ask for help, ignore
small food items, and do not help other ants
0 0 1 if a help message is perceived, go and help, ignore small and
large food items
1 1 0 if a small food item is found, bring it to the nest, if a large
food item is found ask for help, but do not help other ants
1 0 1 if a small food item is found, bring it to the nest, help
other ants, but ignore large food items
0 1 1 if a large food item is found, stay and ask for help, ignore
small food items, and help other ants
1 1 1 if a small food item is found, bring it to the nest, if a large
food item is found, stay and ask for help, and help other ants
The expression of a given behavior bidepends on the number of foragers
already engaged in that behavior and is mediated by the thresholds val-
ues that are genetically encoded, as suggested by the response threshold
value of [4]. For example, if the proportion of members of the team having
activated a given behavior jis smaller than the corresponding threshold
of ant k, behavior bk
jis set to ’1’ (i.e., it is activated).
The agents were not physically simulated; the model assumed a random
walk and took into account the probability of finding a food token at
each time step, which decreased in proportion to the number of token
collected by the agents. The model also included a probabilistic function
of perception and action.
We used 20 agents foraging for 4 large food tokens and 4 small food
tokens. The performance of the robot teams was measured using the
average score obtained during 20 foraging trials. The small food items
provided a score of 1.0 to the single ant who transported it to the nest,
while the large food items provided a total score of 16.0. However, since
the large food items were shared with the whole team, each individ-
ual obtained a score of 0.8 for any large food item taken to the nest.
Fig. 5. Evolution of the mean performance of homogeneous and heterogeneous colonies
under individual and team-level selection (each curve is the average over 10 different
evolutionary runs of mean population fitness).
According to these payoffs, all individuals, including those that do not
cooperate, can get 0.8 points for every large food item transported by
other individuals of the team, whereas the individuals that cooperate in
foraging for large food items, pay a cost of 0.2 points compared to the
score 1.0 that they would made if they foraged on small food items. The
total performance of the team, or total energy brought to the nest, was
highest when individuals were altruist rather than selfish.
Performance differences appeared to be cause mainly by genetic relat-
edness (figure 5). Homogeneous colonies displayed significantly higher
mean fitness than heterogeneous colonies. The difference between ho-
mogeneous and heterogeneous fitness depends on the relative cost and
benefit ratios, as postulated by Hamilton’s inequality. However, there
was no significant difference between the mean performance of homoge-
neous colonies evolved using team-level selection and mean performance
of homogeneous colonies evolved using individual-level selection.
The use of pre-defined behaviors allowed us to precisely measure the
amount of altruistic individuals in the evolving teams in each of the four
evolutionary conditions (figure 6). We considered an individual to be
“altruistic” when it expressed behaviors that did not “pay attention” to
small food items and concentrated only on large food items, either by
searching for large food items or by helping other individuals to transport
large food items (see table above).
Heterogeneous teams, individual-level selection Heterogeneous teams, team-level selection
Homogeneous teams, individual-level selection Homogeneous teams, team-level selection
0102030405060708090 100
0102030405060708090 100
0102030405060708090 100
Fig. 6. Evolution of the frequency of altruistic individuals in the simulated ant popu-
lations (average of 10 runs) given the following experimental setups: (a) Heterogeneous
teams, individual-level selection, (b) Heterogeneous teams, team-level selection, (c) Ho-
mogeneous teams, individual-level selection, and (d) Homogeneous teams, team-level
As expected, the frequency of altruistic individuals within populations
of heterogeneous teams evolved using individual-level selection remained
below 10%. However, in all other three conditions we observed a gradual
dominance of altruistic individuals in the population. In particular, the
resulting number of altruistic individuals is higher when using team-
level selection (Figure 6b and Figure 6d). This is understandable because
team-level selection favors the individuals that work for the team and not
the ones that specialize in the foraging of small food items for their own
This set of experiments indicated that homogeneous teams were con-
ducive to higher performances in a scenario that could benefit from al-
truistic behavior and that team-level selection tended to produce more
altruistic individuals than individual-level selection. Therefore, it came
with no surprise that teams of heterogeneous individuals evolved with
individual-level selection produced very few altruistic individuals and
obtained lower fitness. The question however remained of why hetero-
geneous teams evolved with team-level selection produced a majority of
altruistic agents, but did not result in better fitness than heterogeneous
teams evolved with individual-level selection.
Fig. 7. A team of s-bots engaged in cooperative communication. A team of four s-bots
feed on the food objects while they are lit up in blue color. Two s-bots in white color
are attracted by the blue signal and move away from the poison object.
We will get back to this issue in the next set of experiments where we
repeated our comparison of the four evolutionary conditions in a more
realistic scenario both with physics-based robot simulations and with
real robots.
2.3 Altruistic Communication
The evolution of communication is a particularly challenging problem
both in biological and in robotic systems because efficient communica-
tion requires tight co-evolution between the signal emitted and the re-
sponse elicited [17]. Furthermore, most communication systems are also
costly because of the energy required for signal production [32] and/or
increased competition for resources resulting from the transmitted infor-
mation. For example, if organisms decide to communicate the location of
a limited food source, individuals may pay a cost due to decreased food
intake. In these situations, communication is another example of altruism
and its evolvability and efficiency may depend on the four evolutionary
conditions mentioned above.
We therefore set up an experimental scenario for comparing the four evo-
lutionary conditions where communication provides both benefits and
costs [8]. We used teams of 10 s-bots that could forage in an environ-
ment containing a food and a poison source that both emitted red light
(figure 7). Under such circumstances, foraging efficiency could poten-
tially be increased if robots transmitted information on food and poison
location. However, such communication also incurred direct costs to the
signaler because it resulted in higher robot density and increased compe-
tition and interference nearby the food (i.e., spatial constraints around
the food source allowed a maximum of 8 robots out of 10 to feed simulta-
neously and resulted in robots sometimes pushing each other away from
the food). Thus, while beneficial to other team members, signaling of
a food location effectively constituted a costly act because it decreased
the food intake of signaling robots. This setting thus mimics the natural
situation where communicating almost invariably incurs costs in terms
of signal production or increased competition for resources.
The experiments were conducted multiple times using a physics-based
simulator which accurately models the dynamical properties of the s-
bots. The results were then verified by running a single evolutionary
experiment for each of the four conditions with the physical robots. The
robots had a translucent ring around the body that could emit blue light
and a 360vision system that could detect the amount and intensity of
red and blue light. A circular piece of gray paper was placed under the
food source and a similar black paper under the poison source. These
paper circles could be detected by infrared ground sensors located be-
tween the tracks underneath the robot and thus allowed discrimination
of food and poison.
The robots were equipped with a neural network to process the visual
information and ground sensor input in order to set the direction and
speed of the two tracks and control the emission of blue light accordingly
every 50ms cycle. During each cycle, a robot gained one performance unit
if it detected food with its ground sensors and lost one performance unit
if it detected poison. The performance of each robot at the end of a trial
was computed as the sum of performance units obtained during that
trial (1200 sensory motor cycles of 50ms) and the robot performance
was quantified as the sum of performance units over all 10 trials. Team
performance was equal to the average performance of all robots in the
The feed-forward neural controller had 10 input and 3 output neurons
(figure 8). Once a robot had detected the food or poison source, the
corresponding neuron was set to 1. This value decayed to 0 by a factor of
0.95 every 50ms, thereby providing a short-term memory even after the
robot’s sensors were no longer in contact with the gray and black paper
circles placed below the food and poison. The remaining 8 neurons were
used to encode the 360visual input image, which was divided into four
sections of 90each. For each section, the average of the blue and red
channels was calculated and normalized within the range of 0 and 1,
such that one neural input was used for the blue and one for the red
value. The activation of each of the output neurons was computed as the
sum of all inputs multiplied by the weight of the connection and passed
through the continuous tanh(x) function (i.e., their output was between
1 and 1). Two of the three output neurons were used to control the
two tracks, where the output value of each neuron gave the direction of
rotation (forward if >0 and backward if <0) and velocity (the absolute
value) of one of the two tracks. The third output neuron determined
whether to emit blue light, which was the case if the output was greater
than 0. The genotype of an individual encoded the synaptic weights of
the neural network in a bit string. Each synaptic weight was encoded in
8 bits, giving 256 values that were mapped onto the interval [1,1].
Fig. 8. The neural network architecture used in the experiments on communication.
For each of the four conditions, we ran 20 independent evolutionary
experiments with 100 colonies of 10 robots. Furthermore, as a control
situation, we repeated all experiments (4 times 20 runs) by disabling the
light ring of the robots, but the neural architecture and genotype were
the same as in the normal condition.
To compare team performance between treatments, we calculated the
average performance of the 100 colonies over the last 50 generations for
each of the 20 experiments per condition (figure 9). In evolving teams
where robots could produce blue light, foraging efficiency greatly in-
creased over generations and was significantly greater compared to con-
trol experiments for all evolutionary conditions, except for the condition
of heterogeneous teams under individual-level selection. An analysis of
the robot behavior revealed that this performance increment in the three
conditions of genetic relatedness or team-level selection was associated
with the evolution of effective systems of communication [8].
In teams of genetically related robots with team-level selection, two dis-
tinct communication strategies evolved. In 12 of the 20 evolutionary ex-
periments, robots preferentially produced light in the vicinity of the food
and were attracted by blue light (figure 10, left). Instead, in the other
8 evolutionary experiments, robots tended to emit light near the poison
and were repulsed by blue light (figure 10, right). Teams of robots that
signaled food resulted in higher team performance. Interestingly, once
one type of communication was well established, there was no transition
to the alternate and more efficient strategy. This was because a change
in either the signaling or response strategy would completely destroy
Hom. Het.
Mean performance
No light
With light
Team-level Team-levelInd.-level Ind.-level
p < 0.01 +16.7%
p < 0.01
p < 0.01
p < 0.01
Hom. Het.
Fig. 9. Mean (+ S.D.) performance of robots during the last 50 generations for each
condition when robots could versus could not emit blue light (20 experiments per
the communication system and result in a performance decrease. Thus,
each communication strategy effectively constituted an adaptive peak
separated by a valley with lower performance values.
Heterogeneous teams evolved with team-level selection reliably estab-
lished communication protocols and displayed increased performance
with respect to the control situation. However, their performance was
similar to that of heterogeneous teams evolved with individual-level se-
lection, who did not communicate. This result was analogous to the previ-
ous example where heterogeneous team evolved with team-level selection
displayed a high number of altruistic foragers, but their performance was
similar to that of heterogeneous teams evolved with team-level selection,
who had very few altruistic foragers.
3 Conclusion
We have presented four algorithms for evolving robot collectives that
are presented with situations where altruistic cooperation can lead to a
performance increment. Only three of the four algorithms lead to altru-
istic cooperation, as predicted by kin selection and levels of selection.
Heterogeneous teams of robots evolved with individual-level selection do
not display altruistic cooperation and consequently result in lower fitness
values in tasks that require altruistic cooperation.
Heterogeneous teams evolved with team-level selection represent a spe-
cial case because in both examples they did evolve stable altruistic co-
operators, but their fitness was lower than that of homogeneous teams.
100 0 200150 25050 300 cm
100 0 300 cm200150 25050
Fig. 10. Signaling frequency measured in each area of the arena for robots from two
different evolved teams. a) The team was one where robots signal the presence of food.
b) In this team robots signal the presence of poison. The darkness of each square is
proportional to the amount of signaling in that area of the arena. From [8].
We think that this was due to the fact that after making copies of the
individuals belonging to the best teams, those individuals were mated
with individuals from other teams and randomly re-grouped in new
teams. Although this was biologically plausible and necessary to prevent
the genetic convergence of inbreeding teams, which would have rapidly
led to homogeneous teams and thus confused the experimental design,
it resulted in sub-optimal performance because combinations of well-
integrated diverse individuals were disrupted at every generation.
From a practical perspective, homogeneous teams evolved with team-
level selection are recommended for tasks that can benefit from altruistic
cooperation. Not only do they bring together both conditions for the
emergence of reliable altruism and thus result in higher performance, but
they also do not require the need for separately computing the individual
performance of each individual in a team. This is particularly useful in
robotic tasks where only the resulting work of the team is known, but
not what each robot in the team did and how.
We would like to emphasize that the results described in this chapter are
specific to the case where there is an opportunity for altruistic cooper-
ation and where altruistic cooperation results in higher fitness. We are
currently expanding this line of investigation into three directions. First,
we systematically compare the four evolutionary conditions described in
this paper across experimental scenarios that require different degrees of
cooperation, ranging from simple coordination to cooperation without
a cost all the way to altruistic cooperation. Second, we compare these
evolutionary conditions with other evolutionary methods in tasks that
can benefit from non-trivial division of labor. Third, we compare the four
evolutionary conditions in situations where the individuals in the team
have a specific identity and can recognize each other, which was not the
case in these experiments.
The study of the evolution of robotic collectives is not only promising
for developing efficient control systems and testing biological hypotheses,
but may also have an impact in a larger number of areas that require
an optimal trade-off between the good of the individual and that of
the society, such as internet agents, plant optimization, logistics, and
The authors gratefully acknowledge support from the Swiss National
Science Foundation and from the European projects IST-FET ECAgents
and IST-FET Swarmanoids.
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... But it is only recently that altruistic cooperation has started to draw attention from researchers. Floreano et al. (2008) studied the emergence of altruistic cooperation in swarms of robots foraging for food. Two types of food items are presented to them. ...
... In this experiment, altruistic behavior is expressed by the specific search for large food items, which is highly beneficial to the group while being less rewarding to the individual. By including in their experiment the genetic transmission of behavior traits, Floreano et al. (2008) demonstrate the aforementioned emergence of altruistic behavior in the agent's genes. Clark et al. (2009) propose the concept of a Multi-Robot Community where robots characterized by their level of altruism (will to help others) strive to fulfil their individual tasks. ...
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... Opposite approach can utilize co-evolution of the robots or can exploit some relationship among the robots in the population and evolve robots that exhibit e.g. altruistic behavior [13]. ...
... Opposite approach can utilize co-evolution of the robots or can exploit some relationship among the robots in the population and evolve robots that exhibit e.g. altruistic behavior [13].Fig. 2. Organization of the HyperNEAT substrate. ...
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In this paper we describe simulation of autonomous robots controlled by recurrent neural networks, which are evolved through indirect encoding using HyperNEAT algorithm. The robots utilize 180 degree wide sensor array. Thanks to the scalability of the neural network generated by HyperNEAT, the sensor array can have various resolution. This would allow to use camera as an input for neural network controller used in real robot. The robots were simulated using software simulation environment. In the experiments the robots were trained to drive with imaximum average speed. Such fitness forces them to learn how to drive on roads and avoid collisions. Evolved neural networks show excellent scalability. Scaling of the sensory input breaks performance of the robots, which should be gained back with re-training of the robot with a different sensory input resolution.
... For such functionality, self-sacrifices of individuals often play crucial roles for the benefit of entire population in natural and social systems, such as programmed cell death (Allocati et al., 2015) and altruistic behavior of social insects (Ratnieks and Wenseleers, 2007). Inspired by these biological self-sacrificing mechanisms, several swarm robotic systems incorporating self-sacrifices are beginning to be developed (Sugawara et al., 2017;Floreano et al., 2008). ...
... @BULLET Duplicación: del proceso de replicación debe resultar, al menos, un replicador más. , 2010) y ciertos híbridos entre algoritmos evolutivos y redes neuronales con los cuales se ha visto emerger la capacidad de aprendizaje por imitación en una población de agentes neuronales (Borenstein y Ruppin, 2004) y hasta se ha visto la emergencia de cooperación altruista entre robots como estrategia para lograr beneficio social por encima del beneficio individual (Floreano et al., 2008), lo cual se considera de orden moral y ha fundamentado el desarrollo de las sociedades humanas. Tras considerar el funcionamiento convencional tanto del Perceptrón Multicapa (MLP) como del Mapa Auto-Organizado (SOM), se diseñó una forma de utilizar el conocimiento asimilado por el SOM para entrenar el MLP y así, se armó un modelo híbrido al cual se denominó Perceptrón Auto-Supervisado (SSP). ...
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AI designers endeavour to improve ‘autonomy’ in artificial intelligent devices, as recent developments show. This chapter firstly argues against attributing metaphysical attitudes to AI and, simultaneously, in favor of improving autonomous AI which has been enabled to respect autonomy in human agents. This seems to be the only responsible way of making further advances in the field of autonomous social AI. Let us examine what is meant by claims such as designing our artificial alter egos and sharing moral selves with artificial humanoid devices as well as providing autonomous AI with an ethical framework modelled upon the core aspects of moral selfhood, e.g., making decisions which are based on autonomous law-giving, in Kantian terms.
To parameterize continuous functions for evolutionary learning, we use kernel expansions in nested sequences of function spaces of growing complexity. This approach is particularly powerful when dealing with non-convex constraints and discontinuous objective functions. Kernel methods offer a number of beneficial properties for parameterizing continuous functions, such as smoothness and locality, which make them attractive as a basis for mutation operators. Beyond such practical considerations, kernel methods make heavy use of inner products in function space and offer a well established regularization framework. We show how evolutionary computation can profit from these properties. Searching function spaces of iteratively increasing complexity allows the solution to evolve from a simple first guess to a complex and highly refined function. At transition points where the evolution strategy is confronted with the next level of functional complexity, the kernel framework can be used to project the search distribution into the extended search space. The feasibility of the method is demonstrated on challenging trajectory planning problems where redundant robots have to avoid obstacles.
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Altruism may be found in sets (groups of solutions). In such cases, it may occur that individual/individuals degrade their chances of survival (with sacrifice in the extreme) to ensure survival of fitter individuals. The idea of altruism within group evolution is posed here as a multi objective problem. The aspiration of a group to survive (find an optimal solution) is posed versus the individual's aspiration to survive. In the paper, the problem is a trajectory planning problem with the dilemma producing a Pareto set for a decision maker to choose from. It is shown that if the decision maker is ready to forfeit some of the group members, optimality may be gained. Evolutionary multi objective algorithm is implemented in order to search for this optimal set.
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Experimental issues arise when scientists attempt to directly study emergent behaviour brought on by the evolutionary process. Recently, algorithms that simulate artificial evolution in robotic societies have been used to circumvent such issues. This study attempts to investigate and interpret emergent signals used by artificial agents when evolved through a simple genetic algorithm setup. A multiagent simulation environment is used to model foraging behaviour of artificial agents. Results identify the importance of communication in facilitating co-operative behaviour and reveal interesting convergence in the use of communication signals. Future work is suggested to amend some of the model's drastic simplifications.
This thesis argues that natural complex systems can provide an inspiring example for creating software which incorporates emergent, self-organizing and adaptive properties. The advantages of complex sys- tems are their natural resilience, redundancy and adaptivity. A generalization of neural networks and boolean networks called computational networks is presented as a model for complex systems. It is argued that this model satisfies the required properties for modeling complex systems. Furthermore, it is asserted that a computational network, being a network of mathematical functions, is appropriate for solving classification problems. For the design of computational networks an evolutionary design algorithm is constructed. Additionally, four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. An impor- tant component is a novel generative representation which can reuse substructures of computational networks. Experiments with this component have shown that it facilitates a higher level of complexity in the solution space, improving the computational network performance for more complex problems. Other components steer the evolutionary process towards a desired solution, either by introducing spe- cial stages during evolution, or by smoothing the fitness landscape. The experiments show that complex systems can be evolutionary designed to act as a classifier. The resulting computational network has a better performance on the Iris dataset compared to every classifier in the Weka classifier collection. Furthermore, an experiment was conducted using the TIMIT read speech dataset, the classifier was evo- lutionary designed using only 13 MFCC features, and a very small train set. Although the performance is not good enough to be of any practical use, the results are adequate given the limitations of the train data.
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