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Artificial Ecological Pyramid Model and Its Application in Autonomous Robot Strategy System

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Artificial Ecological Pyramid Model and Its Application in Autonomous Robot Strategy System http://www.paper.edu.cn Artificial Ecological Pyramid Model and Its Application in Autonomous Robot Strategy System Xue Fangzheng, Fang Shuai, Xu Xinhe School of Information Science &Engineering Northeastern University Shenyang, PRC [email protected] Abstract—The Artificial ecological pyramid model is because its discovery is enlightened by ecological pyramid proposed to solve complex problems, which is a layered theory in biology. The most difference between AEPM and evolutionary multi-agent system model. The model has three traditional evolutionary and hybrid evolutionary methods is layers. The lowest layer is composed of “stimulation- that the AEPM considers the hierarchy of intelligence. response” agents called base agents. The second layer is Large numbers of agents with different intelligent level composed of combined agents that are combinations of base collect together to form an agent pyramid like an ecological agents. The top layer is composed of advanced agents that pyramid. The elements of traditional evolutionary methods have reasoning capabilities. Agents in all layers must be up are genes that indicate some information. These genes have against the test of environment. Agents on the lowest layer the simplest intelligence, which are on the lowest layer of can only improve their fitness evaluations by evolutional methods, and agents on upper layers can also improve their the pyramid, so traditional evolutionary method can not fitness evaluations by “eating” agents on lower layers. We have advanced intelligence. The evolutionary neural also construct a strategy system based on artificial ecological network system is equal to an intelligent system composed pyramid model to solve the problem of rivalry between of a neural network agent that has some advance autonomous robots. The simulation results of the test on intelligence and some gene agents that indicate some soccer robot system prove the model effect. information of nodes and structure of neural network. The two kinds of agents are on the top and bottom layer of the Keywords- autonomous robot system, HGA, MAS, soccer pyramid. Apparently, if creatures on the top layer of the robot, strategy system ecological pyramid only eat creatures on the lowest layer, the creatures on the top layer need to eat very much to get I. INTRODUCTION enough energy, perhaps it is not easy to eat or it can cause innutrition. The AEPM has the advantage of great fitness Many people have been looking for a method capability that is also the advantage of traditional intelligent enough to solve all kinds of complex problems. evolutionary methods. If the “eating” algorithm has been The evolutionary method is one of the most outstanding well designed, the AEPM can combine many intelligent one. It is a combination of nature genetics and computer methods and become more intelligent than other methods. science. In 1967, Holland proposed the conception of genetic algorithm [1]. Generally speaking, Holland’s work is called standard genetic algorithm. Many scholars proposed some similar algorithms, for example, Rechenberg and Schwefel’s evolutionary strategy [2] [3], L. J. Fogel’s evolutionary programming [4], Potter. M and K. De Jong’s co-evolution model [5], etc. The developments and applications of these methods make evolutionary method win more and more attentions in AI Fig. 1. An ecological pyramid in biology. area. Even then, the attempts to realize real intelligence by The ecological pyramid has four layers, and is an ecological pyramid about the evolutionary method incur many criticisms. After all, creatures on grassland. such methods cost too many years to realize intelligence in nature. So, people try combining evolutionary methods and II. ECOLOGICAL PYRAMID AND ARTIFICIAL other AI methods, such as evolutionary neural networks [6]. ECOLOGICAL PYRAMID The multi agent theory is an active theory in distributed The ecological pyramid is a conception of biology to artificial intelligence in recent years. It affords us a new describe an ecological system composed of creatures at method to realize hybrid intelligence. The proposed different trophic level. In the ecological system, creatures artificial ecological pyramid model is a multi agent model. are more intelligent and the amount of creatures decreases It is a layered evolutionary pagoda-like model. It is called when the layer become higher. Creatures at the same layer artificial ecological pyramid model (for short, AEPM) 转载 中国科技论文在线 http://www.paper.edu.cn are rivals, and creatures at near layer are hunters and foods problems and becoming more popular for its characteristic (see Fig.1.). of competition and entertainment. A typical soccer robot system (MIROSOT [7]) has four subsystems: computer If we look on the creatures in the ecological pyramid as vision subsystem, strategy subsystem, communication agents, we get a multi agent model called artificial subsystem and car-like robot subsystem. Its operation ecological pyramid model. A three-layer artificial mechanism is that the strategy subsystem makes decisions ecological pyramid model is described in Fig.2. The model by analyzing the positions of moving objects on the ground has three layers: layer of base agents, layer of combined afforded by the computer vision subsystem, and transmits agents, and layer of advanced agents. velocity commands to the home robots via the The layer of base agents (for short, LBA) is composed communication subsystem. Then home robots act of traditional genes. Each agent on the layer is a simple according to the intentions of the system designer. “stimulation- response” agent describes an “if The strategy subsystem is a kernel system of a soccer <condition>then<action>” rule. It is called base agent (for robot system. The strategy procedure of soccer robot short, BA). Traditional genetic algorithms can be used in system is described as below: this layer. N + )1*2( N The layer of combined agents (for short, LCA) has : =→ {}()θθ ∈ RyxyxPVPD ,,,,,, (1), “combined intelligence”. Agents on this layer are = {}(),, ∈ RrvlvrvlvV combinations of base agents, such as rule tree agents, rule network agents, etc. Such agents are called combinational where x, y and θ are the x coordinate, y coordinate and agents (for short, CA). Some custom-built genetic direction of an object, respectively, lv, rv are the velocities algorithms can be used here. For example, if a rule tree of left and right wheel, respectively, P is the position space, agent is described as a “variable length chromosome” agent, V is the velocity space, and N is the amount of robot of one then the “variable length chromosome genetic algorithm” team. can be used. We can get a state S describing the situation of the The layer of advanced agents (for short, LAA) has game from the P space. According to the characteristic of agents that have advanced intelligence. Agents on this layer the soccer robot system, this paper proposes some key are called advance agents (for shoot, AA). Traditional decision-making variables to construct the state. The said S genetic algorithms can not be used here because agents on is a 5-tuple collection, described as below: this layer often have complex structures. S =< bPosition bDirection bSpeed wControl,,,, Agents in different near layers can learn and (2), communicate by the “eating” algorithm. The said “eating” oFormation > algorithm describes the action that an agent on a higher Where bPosition is ball’s position area, bDirection is layer tries to “eat” an agent with some advantages on a ball’s moving direction, bSpeed is ball’s moving speed, lower layer. There are two results after an “eating” wControl indicates which side controls the ball, algorithm. The first is that the agent on the higher layer has oFormation is the formation of the adversary team. part of or all the functions of the agent on the lower layer. The second is that the hunter agent’s fitness value becomes The robot actions are the base of the strategy subsystem, higher and the other one’s become lower. The “eating” including shooting, passing ball, defending, and goal algorithm is not described here, because such algorithms must be variable and be relative to the structure of relevant agents. In the artificial ecological pyramid, the procedure that intelligence comes into being can be looked on as a growing up procedure. Simple reactive intelligence grows up to be advanced intelligence by the “eating” procedure of agents. This procedure can be also looked on as a learning procedure, too. Base agents discover the unknown world by genetic algorithm. Combined agents learn from base agent with high fitness value. Advanced agents learn from combined agents with high fitness value. Comparing with traditional hybrid genetic methods, the AEPM is more flexible and effective because of the existence of LCA. The detailed idea and method of AEPM will be described below by a soccer robot strategy system based on AEPM. III. SOCCER ROBOT STRATEGY SYSTEM Soccer robot system is a multi robots oppositional Fig. 2. The soccer robot strategy system based on the artificial ecological system arisen in recent years. The system has been pyramid model. becoming a standard test-bed for the AI and robotics 中国科技论文在线 http://www.paper.edu.cn keeping, etc. The pivotal attribute of an action is the target. 18-bit binary code, where S occupies 10 bits, and A For example, the target of a pass-ball action is where to occupies 8 bits. pass the ball. If we discretize the pivotal attribute, an action 2) Evaluation method: The system does not always will be divided into multi actions with specific targets. The evaluate after an individual rule was used, because the strategy subsystem’s each decision-making course gets an evaluation of a single rule relies not only on the result after n-tuple collection composed of discrete actions described as below: it executed but also on how tying in with the near rules. We define some specific states when the evaluation events A=< RA1, RA 2 ,..., RAi ,..., RAn > (3), happen.
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