Download computer modelling of neural network of undeveloped living creatures

COMPUTER MODELLING OF NEURAL NETWORK
OF UNDEVELOPED LIVING CREATURES
Ádám Rák1, László Dudás2
1
Student,
Fényi Gyula Grammar School
2
PhD, Associate Professor
University of Miskolc
ABSTRACT
The article introduces results have been achieved in area of computer simulation
of neural network of undeveloped living creatures, namely beetles. For improving the
quality of artificial neural network (ANN) of beetles genetic algorithm (GA) had been
used. In the simplified two-dimensional artificial world of beetles there are two species
of insects: plant-eating group and predacious group. The article discusses the structure
of beetles and theirs artificial micro-world and presents the developed capabilities of
the newer populations of beetles.
Keywords: ANN, GA, multi-agent system, beetle life simulation.
INTRODUCTION
The simulation of multi-agent system is very widespread today and it is in focus of
research of artificial intelligence. Modelling simple biological objects using neural
network is in the front of interest of investigations. The results of a four-year long
research period initiated by one of the author, dr.László Dudás and performed by
Ádám Rák fit into these trends.
ANN and GA integration for artificial life simulation is introduced in [1].
Different aspects of artificial life are discussed in [2].
THE STRUCTURE OF A BEETLE
The applied model of a beetle can be divided into three parts:
1. Sensors
2. ANN
3. Motors.
1. Sensors
The applied “nightly” beetles have two sensors, two feelers that can feel the smell
of food. The right and left feelers give input signal to selected neurons of ANN. The
strength of the signal is calculated as the reciprocal of the distance between feeler and
food. To get stereo effect there is a given distance between left and right feelers that is
applied in calculations but this distance is not applied in the modelling of the beetle,
every beetle and the food are point-like object. The food for a predacious beetle is a
plant-eating beetle. A predacious beetle has sensor for detecting the wall, too.
2. ANN
The starter arrangement is shown in Fig.1. The very simplified model of the beetle
has three neurons: one for directing the forward motion and two for directing the
turning to the left or turning to the right. These two neurons receive the signals of food
sensors and determine the direction of turning. As these neurons compete thanks to
connections having negative weights only one turning direction is possible at given
moment. If the food is exactly in front of the beetle then the two sensors give the same
signal and the result is no turning. The beetle will go straight to the food because every
turning neuron gives signal to the forward motion neuron. The forward motion neuron
gets exiting signal from the turning neurons in every case to induce forward translation
parallel to rotation yielding moving to the food. The beetle cannot go backwards.
Control of forward motion
Control of turning to the right
Control of turning to the left
Input from left feeler
Input from right feeler
Fig.1. The starter ANN
This simplified ANN is the starter network for the beetle at the beginning of its
evolution process. The most important connections wired only, the GA will make
other connections later when new beetle instances are born and the mutation produces
new random connections.
In the more complex view of the ANN can be mentioned the additional
capabilities: every beetle can feel the other beetles using different inputs of the ANN.
Moreover, there is a communication capacity: if the beetles are close to each other
then some of the outputs and inputs of the ANN-s connect to each other.
To avoid go away from the food if it is exactly behind the beetle a little random
movement is added to the food-directed movement.
3. Motors
The motors are the virtual legs realised by software parts that move the drawing of
the beetle on the screen depending on the values of the outputs of the forward motion
and the turning neurons. Similarly, the virtual mouth is a software component that
detects the reaching of the food and eats – clears – that.
THE MICRO-WORLD
The micro-world is not limited theoretically but the random generation of a new
food accomplished in a rectangle area so this motivates the beetles to stay in or close
to this area. If a food is eaten then the world gives a new one in a randomly selected
position. The food is point-like too but a bigger cross represents it on the screen.
There is a straight wall in the middle of the micro-world. This is because of planteating beetles. They can go across the wall without any problem – they cannot detect it
– so they can escape from predacious beetles. The predacious instances have to go
round it. Going across the wall the plant-eating beetles can save their lives and can get
a possibility to have a longer life.
Six plant-eating and five predacious beetles live in this micro-world in the
presented form of the simulation. See Fig. 2.
The wall
A beetle with feelers
The food
Fig.2. The visualised part of the micro-world
THE LIFE AND EVOLUTION OF BEETLES
The life of beetles is managed by the GA. Every beetle has a fitness function. This
can be interpreted as vigour. Eating the food results higher value of vigour while the
progress of time results continuous decrease of it. A bite of a predacious beetle
increases its vigour and decreases the vigour of the attacked plant-eating beetle. The
time is divided into intervals; an interval is the life of a predacious population. At the
end of intervals the GA destroys the weakest instance and generates a new instance
crossing the parameters, neuron weights of the two best members of the predacious
group. In the creation of the new instance some mutation is applied too. This mutation
may result new connections in the ANN and new input neurons and associated
functions can appear.
The evolution of the plant-eating beetles happens when a beetle die so the vigour,
the fitness value reaches a set low value. In this moment a new instance is created
crossing the two best member of the group.
STATIONS OF THE EVOLUTION
These stations can be interpreted as the results of the research. In the beginning the
beetles suffer from different problems: they move too slow, get stuck at wall, lose the
smell of food. Later on after growth of speed the low accuracy causes problem. To
resolve these insufficiencies different forms of behaviour appear, these new motion
forms help the beetle to survive.
When suitable speeds are evolved the next problem appear: the beetle cannot turn
to the food because it is not capable to control the speed volume. The situation: the
beetle circles around the food. See Fig. 3. The other effect of big speed is to miss the
food.
The next phase of evolution resolves these problems: develops the drop-shaped
turn. See Fig. 3.
The curve-shaped path is evolved because the beetle can start to the next food in a
better orientation if the food was reached in the middle of the turning to the next.
Circles around the food
Feeling after wall
Drop-shaped turn
Fig.3. Different evolved forms of behavior
The wall is a problem for the predacious beetle in the beginning. However later
because of evolution they can go around it. A feeling after wall is evolved as a cyclic
moving that looks for the end of the wall. See Fig.3.
The competition for the food develops a new capability: the mutation wires new
neurons having connection possibility to the close beetle. Using these neurons and the
connection to the other member of the group the beetle gives big signal to the other
beetle to disturb the motion of that. It is similar to competing of animals that use
fearful voice to chase away the other animals from the prey. See Fig.4 in the next
page.
CONCLUSION
The paper presented some of the results of simulating a beetle-population having
two species of insects. The multi-agent model applied ANN for modelling the
structure of agents and GA for modelling the behaviour and evolution of them. The
structure had a flexible simple pre-wired initial creature and evolved to a more
complex one that mirrors the requirements of the environment modelled by a microworld and fitness function of agents that can interpret as vigour. Among the results are
special evolved behaviours that rational and similar to behaviour of real biological
beings.
Among the further goals are applying more complex environment, sensors and
motors and studying the evolution of adequate ANN and its capabilities.
Communication is turned off
Communication is turned on
Fig.4. Competition for the food
REFERENCES
[1] Melanie Mitchell, Stephanie Forrest: Genetic Algorithms and Artificial Life (1993)
citeseer.nj.nec.com/mitchell93genetic.html
[2] Artificial Life Online http://www.alife.org/