Evolving Mixed Societies: A one-dimensional modelling
approach

Michael Bodi
∗

Artificial Life Lab of the
Department of Zoology

Karl-Franzens University
Graz,

michael.bodi@uni-graz.at

Martina Szopek
Artificial Life Lab of the
Department of Zoology

Karl-Franzens University
Graz,

martina.szopek@uni-
graz.at

Payam Zahadat
Artificial Life Lab of the
Department of Zoology

Karl-Franzens University
Graz,

payam.zahadat@uni-
graz.at

Thomas Schmickl
Artificial Life Lab of the
Department of Zoology

Karl-Franzens University
Graz,

thomas.schmickl@uni-
graz.at

ABSTRACT
Natural self-organising collective systems like social insect
societies are often used as a source of inspiration for robotic
applications. In return, developing such self-organising ro-
botic systems can lead to a better understanding of natural
collective systems. By unifying the communication chan-
nels of the natural and artificial agents these two collective
systems can be merged into one bio-hybrid society. In this
work we demonstrate the feasibility of such a bio-hybid so-
ciety by introducing a simple one-dimensional model. A set
of patches forms a one-dimensional arena, each patch rep-
resents a stationary robot, which is controlled by an AHHS
(Artificial Homeostatic Hormone System) control software.
The stationary robots are able to produce different types
of environmental stimuli. Simulated bees react diversely to
the different stimuli types. An evolutionary computation
algorithm changes the properties of the AHHS and defines
the interactions between the robots and their properties of
stimuli emission. The task is an aggregation of simulated
bees at a predefined aggregation spot. We demonstrate that
an evolved AHHS is a very feasible tool for controlling these
stationary robots. Furthermore we show that an AHHS even
works robustly in different setups and dynamic environments
even though the controller was not specially evolved for these
purposes.

∗Corresponding author

.

Categories and Subject Descriptors
A.1.3 [General and reference]: Document types—Gen-
eral conference proceedings; F.5.9.2 [Theory of computa-
tion]: Design and analysis of algorithms—Distributed algo-
rithms, Self-organization; K.3.7.1 [Computing method-
ologies]: Artificial intelligence—Distributed artificial intel-
ligence, Multi-agent systems; K.3.7.3 [Computing method-
ologies]: Artificial intelligence—Distributed artificial intel-
ligence, Mobile agents; K.7.1.2 [Computing methodolo-
gies]: Distributed computing methodologies—Distributed al-
gorithms, Self-organization

General Terms
Algorithms

Keywords
mixed-societies, bio-hybrid systems, evolutionary computa-
tion, multi agent systems, swarm robotics

1. INTRODUCTION
Natural self-organising collective systems like social insect

societies are often used as a source of inspiration for robo-
tic applications. In return, developing such self-organising
robotic systems can lead to a better understanding of the
natural collective systems. By unifying the communication
channels of the natural and artificial agents these two collec-
tive systems can be merged into one bio-hybrid society. Biol-
ogy knows many examples of collective behaviour on a wide
range of organisational complexity, from organisms as simple
as slime moulds (Nakagaki, 2001) or bacteria (Camazine
et al., 2001) via insects and fish to highly sophisticated rep-
resentatives of birds and mammals. The most intriguing
example for the efficiency of collective behaviour is found
in social insects. For example in ants, detailed research has
been conducted on phenomena like chain formation (Lioni
et al., 2001) and bridge formation (Deneubourg et al., 1990).

BICT 2015, December 03-05, New York City, United States
Copyright © 2016 ICST
DOI 10.4108/eai.3-12-2015.2262514



One of the most studied social insects is the European hon-
eybee Apis mellifera. Honeybees are not only crucial for
agricultural and economical purposes (e.g. pollination), but
have also successfully found their way into engineering ap-
proaches. For example, honeybees inspired the development
of bio-inspired algorithms for controlling autonomous swarm
robots (Schmickl et al., 2008; Bodi et al., 2012, 2011).

Robotic devices also become more important in biologi-
cal studies. The simplest form for such robotic applications
would be a sensor network for monitoring animals (Zacepins
et al., 2011). A more ambitious approach is the usage of ro-
botic agents in animal societies for the purpose of influenc-
ing the animal behaviour (Halloy et al., 2007). The ongoing
EU-project ASSISIbf presents a new approach for closing
the loop of interaction between natural and artificial agents
for the purpose of forming a bio-hybrid mixed society con-
sisting of animals (honeybees Apis mellifera) and stationary
autonomous robots called CASUs (Combined Actuator Sen-
sor Units)(Schmickl et al., 2013b,a). CASUs are equipped
with several sensors (e.g., temperature sensors, proximity
sensors) (Salem and Schmickl, 2014) and actuators (e.g.,
heating devices, vibration devices). This system should be
able to adapt without a priori knowledge of the open system
as we are using evolutionary computation for adapting the
interface between the natural and the artificial society with
the goal to produce common collective behaviours.

For closing the loop of interaction between bees and CA-
SUs, it is first necessary to find a set of different stimuli,
to which bees show different behavioural responses. It is
already known, that honeybees are collectively attracted to
temperature (Szopek et al., 2013). Finding other appro-
priate stimuli types is part of the research in the ASSISIbf
project. Figure 1 shows a preliminary experiment using real
honeybees and CASU prototypes. In this preliminary exper-
iment the CASUs use temperature to pull the bees from the
left to the right side of an experimentation arena. The tem-
perature settings were controlled solely by the experimenter
with no autonomy of the CASUs or interaction between the
CASUs and the honeybees. The approach of the ASSIS-
Ibf project is to form such a bio-hybrid society of bees and
CASUs by using evolutionary computation. These evolved
controllers should learn to use the different stimuli to al-
ter the behaviour of the bee collective (e.g., aggregation,
separation, path following) Therefore we decided to test in
simulation, whether or not, the reaction-diffusion controlled
Artificial Homeostatic Hormone System (AHHS) controller
is the right tool for this task.

The aim of this work is to evolve a controller for such
a bio-hybrid system consisting of two types of autonomous
agents: predictable but unprogrammable agents (simulated
honeybees) and programmable and evolvable agents (simu-
lated CASUs). We answer the following questions: (I) Is it
possible to evolve a controller to redirect reactive agents by
the use of different types of environmental stimuli? (II) Is
an AHHS controller, evolved for a specific setup, able to per-
form tasks in a more general environment? To answer these
questions, we developed a one-dimensional proof of concept
model of a bio-hybrid system in Netlogo (Wilensky, 1999).

2. METHODS
A set of 101 patches, located in a straight line, represents

a one-dimensional experimentation arena (figure. 2). Each
patch can be considered as one CASU (Combined Actua-

Figure 1: Snapshots of a preliminary experiment with real
CASUs and a group of honeybees. (A) The left CASU was
heated up to 36 ◦C (preferred temperature of young bees).
After the bees aggregated there, the left CASU was cooled
down and the right CASU was heated up to 36 ◦C. The
majority of the group followed the optimum, but some bees
remained at the left CASU (B).

tor Sensor Unit) which is able to generate three different
stimuli types via implemented actuators. The CASUs are
controlled by an AHHS control software which controls the
’behaviour’ of the CASUs by perceiving concentrations of
virtual hormones and reacting to them according to a set of
rules. These rules can then be changed by an evolutionary
computation algorithm.

2.1 The AHHS control software
AHHS (Artificial Homeostatic Hormone System) (Schmickl

and Crailsheim, 2009; Schmickl et al., 2011) is a reaction-
diffusion-based system inspired by the Turing process (Tur-
ing, 1952) which describes processes of natural pattern for-
mation and growth. AHHS has already been successfully
implemented in robotic applications (Stradner et al., 2009;
Schmickl et al., 2010; Hamann et al., 2010) and has been in-
vestigated in terms of pattern formation and diversity gen-
eration capabilities (Zahadat et al., 2013). An AHHS is de-
fined by a set of artificial hormones and a set of rules. The
rules define how sensory inputs and hormone concentrations
participate in making changes in hormone concentrations
and outputs of the system (see figure 3). Both, hormones
and rules, may be changed by an evolutionary process.

In our one-dimensional model we use 8 different hormones
H which can have values between 0 and 255. H1 forms a
prime gradient from left to right, meaning that on the left
side H1 has a value of 255 and on the right side a value of
0. This gradient feeds spacial information into the system.
H2 has a constant value of 127 across all of the arena with
exception of patches -5 to 5. Within this area H2 generates
‘white noise’ (figure 4), meaning that every patch from -5



Figure 2: Screenshots of the one-dimensional experimentation arena. Yellow dots represent honeybees. The green area
represents the target for the bees. The purple area represents a mixture of two environmental stimuli: red = temperature,
blue = light. This screenshot shows the different behaviours of bees and CASUs for an unevolved (random) (A) and an evolved
(B) genome.

Figure 3: Graphical representation of the AHHS and the
interactions between hormones inside an AHHS, the inter-
action between two AHHS controllers and the interactions
between AHHS and CASUs. H1 - H4 represent different
artificial hormones and their interactions. H4 controls the
properties of the CASU (located above H4).

to 5 produces a random hormone value in every time step.
This area of white noise marks the aggregation spot for the
honeybees. The hormones H3 - H5 are used to control the
actuators within the CASUs, H3 controls an attractive stim-
ulus A, H4 controls a repelling stimulus B and H5 controls
a stumulus C which servers a stopping signal. H6 - H8 serve
as ‘free hormones’ and are used by the AHHS for computing.

2.2 Stimuli and Honeybee model
Each patch contains 3 actuators which act as the sources

of 3 different types of stimuli. These stimuli types are fur-
ther called type A, B, and C and are different in terms of
their physical properties and the reaction of the simulated
bees to them (see Table 1). Stimulus A serves as an attrac-
tive signal (e.g., temperature and chemicals) and stimulus B
serves as a repulsive signal (e.g., light, airflow). This means
that a bee tends to move to a neighbouring patch if the in-
tensity of stimulus A is higher or the intensity of stimulus
type B is lower in the neighbouring cell. Stimulus C serves
as a stopping signal (e.g., vibration), meaning that if the
intensity of the stimulus is above a certain threshold value
the bee stops on the cell, ignoring the attractive or repulsive
stimulus. It’s noteworthy, that the assumed reactions of the
simulated bees to the mentioned stimuli in this model are
more or less arbitrary. These reactions do only have the pur-
pose to test the suitability of the described algorithms. It is
part of the ASSISIbf project to determine the behavioural
response of the honeybees on different environmental stimuli
in laboratory experiments.

As already mentioned, the CASUs are able to generate 3
different types of stimuli and the simulated bees react differ-
ently to each type of stimulus. A bee moves to a neighbour-
ing patch, called newpatch, based on following equation:

Figure 4: Hormone levels of the prime gradients used in
our model. The x-axis represents the space of the one-
dimensional experimentation arena. Hormone H1 generates
a gradient throughout the arena. Hormone H2 is used as a
marker for the target area by producing white noise at this
location.

attri = (Ai −Acurr) + (Bcurr −Bi), (1)

newpatch =

{
arg maxi attri, if attri > 0 and Ccurr < th

curr, otherwise
,

(2)
whereby curr is the current patch and Ai, Bi, and Ci are

the intensities of the stimuli A, B, and C in patch i. In this
experiment we use a simplified simulation of temperature,
vibration and light as examples of stimuli of types A, B,
and C. The intensities of the stimuli and the increase value
of the stimuli in each time-step are both limited between 0
and 1. The threshold value of the vibration is set to 0.1.
The values of all parameters used in this simulation can
be found in table 1. In this model the bees do not have
any physical properties, which means that the bees are able
to move through each other and can stack up on a single
patch. We made this decision because real honeybees can
form very dense aggregations and sit on top of each other.
It’s also noteworthy, that the parameters of the stimuli do
not precisely correspond to reality and the effects of these
stimuli on the bees are assumed to be linear for simplification
of the simulation, since the purpose of this simulation is to
test the before mentioned algorithms.



Table 1: Characteristics of the stimuli used in the models.

Stimulus A (Temperature) B (Light) C (Vibration)
Effect attractive repellent stop-signal
Diffusion rate 0,2 0 0,01
Decay rate 0,1 1 0,9
Instantly reachable no yes no
Blockable by bee no yes no

Figure 5: Graphical representation of the feedbacks within
our model.

2.3 Experimentation

2.3.1 Experiment I:
The goal of this experiment is to evolve an AHHS con-

troller that leads the bees to form an aggregation at a pre-
defined target area which is marked by white noise of hor-
mone H2. At the beginning, 11 bees are placed equally dis-
tributed within the arena. These bees move around in the
arena according to their reaction to the presented stimuli.
The genetic start population used in these evolutionary runs
consist of 30 random genomes. For evolving the AHHS con-
troller, a wolfpack-inspired evolutionary algorithm is used
(Zahadat and Schmickl, 2014). This algorithm uses overlap-
ping generations and a fixed population size representing a
limited resource (e.g. food). The offspring is evaluated and
ranked hierarchically according to their fitness. An eval-
uated offspring removes another individual with equal or
lower fitness from the population. This way the population
stays dynamic.

The fitness function in the presented model was defined
as

fitness = n
2
t +

∑(x−dt
4

)
, (3)

x =

{
2dmax if dt = 0
dmax otherwise

, (4)

where nt is the number of bees in the target area, dt is the
distance of a bee to the centre of the target area and dmax
represents the maximum possible distance to the centre of
the target area. This means that bees which aggregate in the
exact centre of the aggregation spot gain more fitness, than
bees which locate themselves merely within the aggregation
area. The evolutionary runs were repeated 7 times for 151
time steps each.

2.3.2 Experiment II:
After the evolutionary runs, we picked the genome with

the highest fitness and tested it in terms of robustness and
generality. In this experiment we varied the number of bees
in the arena and observed the number of aggregated bees in
the target zone. The tested groupsizes were 2, 5, 7, 11, 15
and 20 bees. The experiments were repeated 24 times.

2.3.3 Experiment III:
In this experiment we tested the ability of the system to

redirect the bees in a dynamic environment. Therefore we
started with 11 randomly distributed bees and the aggrega-
tion spot placed off-centre. After 150 time steps we changed
the position of the aggregation spot. In one setup the aggre-
gation spot jumped abruptly (figure 9a and 9b), in another
setup the aggregation spot moved continuously to its new
position (figure 9c and 9d). We monitored the number of
aggregated bees after 350 time steps. The tested gap widths
between the relocated aggregation spots were in both se-
tups 11, 21, 27, 29 and 31 patches. The experiments were
repeated 24 times.

3. RESULTS

3.1 Experiment I:
The evolutionary computation algorithm was tested with

11 equally distributed bees. Their task is to aggregate in
the target zone located in centre of the arena, marked by
white noise of hormone H2. The system started with ran-
dom genomes. As it is shown in figure 6, the fitness rises
quickly. After the first evaluation the median fitness is 64
with a maximum fitness of 124,5. After 500 evaluations the
median fitness already reaches 357,75 and the maximum fit-
ness reaches the possible maximum fitness of 396. After 1500
evaluations the highest median fitness is reached with 383,25
and all but one genomes are capable of positioning the bees
in the target-zone. After 2000 evaluations all genomes man-
age to relocate all bees in the target zone.

3.2 Experiment II:
We tested the generality of the fittest genome by varying

the number of bees. As we show in figure 7 the evolved
genome can handle different groupsizes of bees very well.
The evolved genome is able locate 100% of the bees in the
target zone, independent of the tested groupsizes.

3.3 Experiment III:
We tested the fittest genome in a dynamic environment.

After 150 time steps we switched the position of the target
zone and after 350 time steps we observed the number of
bees in the new positioned target zone. As we show in fig-
ure 8 the evolved system is flexible enough to move 100%
of the bees to the new target area when the gap between



Figure 6: Results of experiment I: This figure shows the
median fitness (with min, max, q1, q3) of the best genome of
each evolutionary run (N = 7 repetitions). fmax represents
the maximum possible fitness when all bees aggregate at the
exact centre of the target area. fall inn represents the fitness
when all bees aggregate in the target area (For details on the
fitness function see equation 3 and 4).

Figure 7: Results of experiment II: Mean fraction of bees
(with stdev) aggregated in the target area. (N=24 repeti-
tions/experiment)

the targets is between 11 and 21 patches. Once the gap ex-
ceeds 27 patches, the number of aggregated bees drops. But
still, looking at the 27 patch gap, an average of 96.7% (stdev
±6,4) of the bees are aggregated in the target area. Beyond
that distance the aggregation drops rapidly. With gaps of
29 and 31 patches only 13.3% (stdev ±12) and 9,6% (stdev
±7,5) of the bees aggregate at the new target area, respec-
tively. When the target is not moved abruptly but continu-
ously, 100% of the bees could be pulled into the repositioned
target area, independent of the gap width.

4. DISCUSSION

4.1 Experiment I:
As we have shown in figure 6 the median fitness is 357,75

after already 500 evaluations. At this point also the maxi-
mum fitness of 369 is reached by at least one genome. After
2000 evaluations all evolved genomes reach high fitness val-

Figure 8: Results of experiment III: Mean fraction of bees
(with stdev) aggregated in the target area after it has been
moved to a new position. The solid line represent the re-
sults for abrupt target changes, the dashed line represents
the result for continuous target changes. (N=24 repeti-
tions/experiment)

(a) Gap of 10
patches

(b) Gap of 20
patches

(c) Gap of 10
patches

(d) Gap of 20
patches

Figure 9: Example screenshots of experiment III: sponta-
neously and continuously moving targets with a gap of 11
and 21 patches. The yellow lines indicate the trajectories of
the bees. The green areas represent the target areas. The
stimuli are represented by a mixture of red (temperature)
and blue (light).



ues (median fitness: 383,25) and are able to pull the bees
into the target area. The steep increase of fitness and the
good fitness performance strongly indicate that the combi-
nation of our evolutionary computation algorithm and the
AHHS control software is a feasible tool for developing robot
that can learn to manipulate animals.

4.2 Experiment II:
As we have shown in figure 7 an evolved AHHS can use

the CASU’s actuators to lure the bees to a desired location.
Although the AHHS controller was evolved for groups of
11 bees it is able to fulfil this task also with other group
sizes. We tested group sizes between two and 20 bees and
in all cases 100% of the bees were directed to the target
area. Since we did not model any interaction between the
bees, they can be considered as an indirect probe of the
CASU stimuli landcape. Therefore this result is not really
surprising. Nevertheless this result serves us as a sanity
check of our model.

4.3 Experiment III:
When the target area changes its position abruptly, an

evolved AHHS is capable to redirect the bees into the new
target unless the gap between the two targets gets too big.
As we have shown in figure 8, a gap of 11 to 21 patches can
be vanquished without any losses to the aggregation prop-
erties. All bees can be redirected into the new target area.
When the gap surpasses 20 patches, the number of bees lo-
cated in the new target decreases. Reaching a gap distance
of 27 patches the number of redirected bees starts to decrease
slightly, but still 97.7% of the bees could be redirected. At
a gap distance of 29 patches and above the number of redi-
rected bees decreases significantly. This can be explained by
the properties of AHHS, as a hormone gradient triggers the
actuators of the CASUs. Since the evolution of AHHS was
performed with a target area located in the centre of the
arena, the hormone gradient does not spread far enough to
overcome the gap. But when the target area does not jump
abruptly to a new position, but continuously moves there,
all bees can be redirected to the new target, no matter how
far the gap between the target is. This can be explained by
the fact, that not only the target moves continuously to the
new position but also the hormone gradient does. This way
the bees can easily be ‘pulled’ to a new location.

5. CONCLUSION AND FUTURE WORK
In this work we answered the question if an evolved AHHS

is suitable for controlling CASUs and luring simulated hon-
eybees into a predefined target zone. Therefore we gener-
ated a model of a one-dimensional experimentation arena
containing CASUs, controlled by an AHHS controller. We
showed that the combination of the wolfpack inspired evo-
lutionary algorithm and AHHS is very promising tool for
this tasks. We also answered the question if an AHHS con-
troller, evolved for a specific setup, is able to perform tasks
in a more general environment. Once evolved, AHHS is ca-
pable of reacting to dynamical environmental changes. Our
results strongly indicate that an evolved AHHS is a flexible
and reliable tool and a promising approach for controlling
stationary autonomous robots to influence the behaviour of
simulated honeybees.

We are well aware that, because of the strong simplifi-
cation of the bee model and the stimuli model, there’s a

reality gap from our modelling approach to the real world
setup. Nevertheless, our results indicate that the observed
algorithms are a feasible choice for the real world problem.

In the future we plan to extend our model by implement-
ing the actual physical properties of the stimuli used. Once
we have experimentation data of how honeybees react to the
given stimuli types, this will also be implemented into our
model. Furthermore we will transfer the gained knowledge
to the real world and test our approach on real CASUs with
real honeybees.

6. ACKNOWLEDGEMENTS
This work was supported by: EU-ICT ‘ASSISI|bf’, no.

601074.

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