COMPUTER MODELLING OF NEURAL NETWORK OF UNDEVELOPED LIVING CREATURES

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1 ABSTRACT COMPUTER MODELLING OF NEURAL NETWORK OF UNDEVELOPED LIVING CREATURES Ádám Rák 1, László Dudás 2 1 Student, Fényi Gyula Grammar School 2 PhD, Associate Professor University of Miskolc 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

2 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 left Control of turning to the right 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.

3 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. A beetle with feelers The wall 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.

4 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

5 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