• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1154-1160
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• 1999

A rule based controller is constructed for multiple robots accomplishing a given task in unknown environments by using genetic programming. The example task is playing a simplified soccer game, and the controller for robots that governs emergent cooperative behavior is successfully found using the proposed procedure A neural network controller constructed using the rule based controller is shown to be applicable in a more complex environment.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2197-2199
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• 1998

Many multiagents cooperative problems, such as table transport problem, require several emergent behaviors and a proper coordination of these is essential for successful accompishment of the task. We study in this paper the genetic programming method, called fitness switching, to evolve cooperation strategies of robots in these kind of tasks and show simulation results to demonstrate its effectiveness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.170-175
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• 1998

Transporting a large table using multiple robotic agents requires at least two group behaviors of homing and herding which are to bo coordinated in a proper sequence. Existing GP methods for multi-agent learning are not practical enough to find an optimal solution in this domain. To evolve this kind of complex cooperative behavior we use a novel method called fitness switching. This method maintains a pool of basis fitness functions each of which corresponds to a primitive group behavior. The basis functions are then progressively combined into more complex fitness functions to co-evolve more complex behavior. The performance of the presented method is compared with that of two conventional methods. Experimental results show that coevolutionary fitness switching provides an effective mechanism for evolving complex emergent behavior which may not be solved by simple genetic programming.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.149-155
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• 2003

In this study, we introduce the main ideas on the control and strategy used by the robot soccer team of the Universidad de Buenos hires, UBA-Sot. The basis of our approach is to obtain a cooperative behavior, which emerges from homogeneous sets of individual behaviors. Except for the goalkeeper, the behavior set of each robot contains a small number of individual behaviors. Basically, the individual behaviors have the same core: to move from the initial to-ward the target coordinates. However, these individual behaviors differ because each one has a different precondition associated with it. Each precondition is the combination of a number of elementary ones. The aim of our approach is to answer the following questions: How can the robot compute the preconditions in time\ulcorner How are the control actions defined, which allow the robot to move from the initial toward the final coordinates\ulcorner The way we cope with these issues is, on the one hand, to use ball and robot predictors and, on the other hand, to use very fast planning. Our proposal is to use planning in such a way that the behavior obtained is closer to a reactive than a deliberative one. Simulations and experiments on real robots, based on this approach, have so far given encouraging results.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

• Korean Journal of Construction Engineering and Management
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• v.14 no.6
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• pp.49-60
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• 2013

Evacuation process has been considered as one of the most important elements to be managed in public facilities. Although the importance is highlighted through numerous literatures, disaster evacuation planning, particularly fire accidents, faces a number of human behavior related limitations for a similar application to different types of facilities/occupants. To overcome the obstacles including complexity in human behaviors, a number of simulation techniques with limited consideration on human behaviors are utilized to predict foreseeable problems in evacuation process. Therefore, this research aims to propose system dynamics models incorporating human behaviors considering different types of occupants under disaster evacuation events. Analysis on emergent human behaviors such as group forming and interactions under urgent situation are conducted based on the main stream theories in social science field. The results suggest the influences of human behavior factors including cooperative intention, information sharing, and mobility change to evacuation behavior. The implications are expected to provide safety consideration at planning/designing phase of buildings and help facility safety managers for evacuation planning with more realistic management approaches.