ࡱ> Y[X7 06bjbjUU OR7|7|+ltttt$(4444H4(62G'G'G'6666666$ 8 ):(6iG'q&G'G'G'(6*tt6***G't6*G'6**05|L5 | L,( 4)5560656=;*=;5*((ttttThe 11th INTERNATIONAL DAAAM SYMPOSIUM   Intelligent Manufacturing & Automation: Man  Machine  Nature  19-21st October 2000 some considerations of the development of social behavior based robot Jerbi, B. Abstract: This work considers some approaches to the development of the autonomous robot with social intelligence. The presented concept involves the model of control system built from several intelligent agents. Each produces specific robot behavior with regard to the assigned role and character. Such scheme is a basis for the creation of the collaborative control system which could provide the achieving of very complex intellectual capabilities. Key words: robotic assembly, multiagent system, autonomous agent, learning methods. 1 INTRODUCTION Why people think computers cant, this is the question that Marvin Minsky asked himself in 80s (Minsky, 1982), synthesizing the crucial issues of all AI researchers. He analyzed some human intellectual characteristics, like: creativity, ability of learning, problem solving, understanding and being self-aware (conscious), trying to demystify some of the notions and to discover the directions where AI should research for the solutions. The conclusion of his contemplations could be that really intelligent computer (robot, agent) should understand itself, its knowledge, enough to change (improve) itself: To me there is a special irony when people say machines cannot have minds, because I feel were only now beginning to see how minds possibly could work - using insights that came directly from attempts to see what complicated machines can do. Of course were nowhere near a clear and complete theory - yet. But in retrospect, it now seems strange that anyone could ever hope to understand such things before they knew much more about machines. Except, of course, if they believed that minds are not complex at all. It will be a long time before we learn enough about common sense reasoning to make machines as smart as people are. Today, we already know quite a lot about making useful, specialized, expert systems. We still dont know how to make them able to improve themselves in interesting ways. But when we answer such questions, then well have to face one, even stranger, one. When we learn how, then should we build machines that might be somehow better than ourselves? Were lucky that we have to leave that choice to future generations. Im sure they wont want to build the things that well unless they find good reasons to. Just as Evolution changed mans view of Life, AI will change minds view of Mind. As we find more ways to make machines behave more sensibly, well also learn more about our mental processes. In its course, we will find new ways to think about thinking and about feeling. Our view of them will change from opaque mysteries to complex yet still comprehensible webs of ways to represent and use ideas. Then those ideas, in turn, will lead to new machines, and those, in turn, will give us new ideas. No one can tell where that will lead and only one things sure right now: theres something wrong with any claim to know, today, of any basic differences between the minds of men and those of possible machines. Thus, the idea of intelligence, as something what assumes the understanding, creativity and self-improving, should rely on the learning skills. The magic of intelligence is actually evolving in long time learning process. Our genetic profile is dominant particularly in the domain of learning (Jerbi et al.., 1999; Mataric, 1994). At the same time, it seems that learning models are more obvious, more understandable, since we are able to observe them in contrary to hidden unconscious brain logics and activities (Canamero, 1996). Hence, it is much more effective to build the learning system capable to design the corresponding intellectual mechanism on its own. The system of this kind can be understood as tought or trained, instead being programmed. The implementation of learning capacity of an agent, robot or computer represents the basis for the achievement of its intelligent autonomous behavior. 2 AUTONOMOUS ROBOT The autonomous robot means a working device with certain motoric capability (motion and/or moving), which implements a generative planning system that interacts with a changing environment in real time. The given definition actually describes an intelligent agent with embedded dynamic-reaction model based on belief-desire-intention architecture. It is usually called softbot, behavior based robot or decision making rational being. In the terms of automatic system, the autonomous agents should provide adaptation and planning methods as the answers to the nondeterministic dynamism of working environment. It can be said that mechanisms of learning, and the complex balance between what is learned and what is genetically determined, are the main concern of behavioral sciences (). Talking about intelligence and behavior we mustnt forget the fact that the social behavior is the highest form of intelligence. In the term of robotics it means that the robot can be understood as multiagent device, which is either the system of more than one intelligent device (several robots coupled as social modus operandi) or simply the society of multiagent control subsystems (the robots intelligent control system decomposed into the multiagent control subsystems). 3 MULTIAGENT SYSTEM Any multidevice system or any system whose performance is naturally decomposable is multiagent system. It means that even one robot can be controlled by several programs or computers which, collaborating each to other, construct complex intellectual behavior. The social intelligence can provide many advantages: improved system performance by exploiting the parallelism of sensing and action, collective decision making based on distributed criticism, contributing the creative construction of ideas how to solve new problems, reliability which comes from the agent redundancy and reduction of individual complexity. 4 MULTIAGENT SYSTEM AS SOCIAL MODUS OPERANDI The main parameters of multiagent system are social organization and communication. The social organization defines the structure of a robotic society which specifies system hierarchy, agent responsibilities and roles: leaders, followers, opponents, helpers, teachers, The social organization directly affects on the behavior of multiagent system. Some of the agents will follow and help each other, some of them will look for optional ways, some will take care of agent security, the other will suggest congregation or will incline to cover wider working area. The arbitration function evaluates the behavior of the each agent and the progress of the whole system. It shows when some behavior have resulted with good actions which should be learned as positive experience, or alerts when bad behavior produced undesired effects. The communication between agents is a basis of agent community. Without the ability to communicate the agents cannot create community. The communication system must define: whether to communicate, what is the information content, who are the members in communication, what is the range of communication. The communication should be established on the basis of perception, role and current position/state. For example, some of the agents could not be allowed to initiate communication, because they are not the leaders or responsible agents; some of the agents could be out of the communication range, i.e. their position or state does not give them a chance to be efficiently used for the solving of the encountered problem. The versatility of the communication rules and their combinations give the opportunity to create very complex and creative multiagent social behavior which can roll out the robust problem solving engine. 5 MODEL OF MULTIAGENT CONTROL SYSTEM The parts of the intelligent robot are: robot itself with the motion devices (joints, wheels, drives, ), perception system (global, local), intelligent control system, including: solution searching algorithm, learning mechanism, associative experience assigning system. The assumption is that the robot is controlled by several intelligent agents. Each produces specific behavior with regard to the assigned role and character. Together they constitute behavior system. For the simple model of mobile robot, which is supposed to avoid obstacles moving toward a given target, the behavior system could be composed as described in Fig. 1.       Fig. 1. Multiagent behavior system for the mobile robot The resolute agent tends to guide the robot directly to a goal. The fearful agent is likely to run away from the obstacles, even it moves the robot far from the main direction. The empirical agent forces the application of the acquired experience, actually the stored knowledge about the similar problems. Even using simple combinations among the influence of particular agents the versatile behavior of the robot could be provoked, as illustrated in Fig. 2.  Resolut agent Fearful agent Empirical agent domination Fig. 2. Various robot behaviors depending on the particular agent domination Besides, varying the contents and policy of interaction and the rules of synthesis, very complex behavior could be produced and adjusted for the solving of very complicated problems. 6 CONCLUSION The paper has discussed the development of social behavior based robot. Such approach comprises the robot control system built from several collaborative intelligent agents which can provide the achieving of the social intelligence. Comparing to usual AI methods in robotics it has certain advantages: the AI system is designed from simple intelligent control programs which can be efficiently created, simple control programs assure the robustness of the whole system, the versatility of communication rules and their combinations give the opportunity to create very complex and creative multiagent working behavior. 7 REFERENCES Canamero, D. (1996). Modeling Motivations and Emotions as a Basis for Intelligent Behavior, Massachusetts Institute of Technology, AI memo 1597. Jerbi, B., Grolinger K. & Vranjea B. (1999). Autonomous Agent Based on Reinforcement Learning and Adaptive Shadowed Network, Artificial Intelligence in Engineering, Vol. 13, No. 2, (February 1999) 141-157, ISSN 0954-1810. Jerbi, B. (1999) Collaborative Behavior of Multiagent System in Robotic Assembly, Proceedings of the 10th DAAAM International Symposium, Katalinic, B. (Ed.), pp. 231-232, ISBN 3-901509-10-1000, Vienna, October 1999, DAAAM International, Vienna. Mataric, M.J. Interaction and Intelligent Behavior, MIT, AI Technical Report 1495, August 1994. Minsky, M. (1982) Why people think computers cant, AI Magazine, Vol. 3, No. 4, (Fall 1982). Author: Assoc. Professor Bojan Jerbi, Ph.D., University of Zagreb, Faculty of Mechanical Engineering, I. 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