In this article we introduce a blackboard- based multiple agent system framework that considers biologically-based motivations as a means to develop a user friendly interface. The framework includes a population-based heuristic as well as a fuzzy logic- based inference system used toward scoring system behaviours. The heuristic provides an optimization environment and the fuzzy scoring mechanism is used to give a fitness score to possible system outputs (i.e. solutions). This framework results in the generation of complex behaviours which respond to previously specified motivations. Our multiple agent blackboard and motivation-based framework is validated in a low cost mobile robot specifically built for this task. The robot was used in several navigation experiments and the motivation profile that was considered included "curiosity", "homing", "energy" and "missions". Our results show that this motivation-based approach permits a low cost multiple agent-based autonomous mobile robot to acquire a diverse set of fit behaviours that respond well to user and performance expectations. These results also validate our multiple agent framework as an incremental, flexible and practical method for the development of robust multiple agent systems
Los autores exponen los resultados de una investigación sobre la presencia del peritaje antropológico en la defensa penal en Chile en contextos de diversidad cultural intra-social. Se analizaron causas penales periciadas respecto de imputados/as no indígenas. Se estableció la utilización de esta herramienta en contextos campesinos y urbanos, así como en conflictos inter-personales e intra-familiares. Se concluye que el peritaje antropológico sirve para visibilizar y, de este modo, afirmar de manera científica las diferencias socioculturales existentes en la sociedad chilena, en contextos extra-indígenas. El valor de este trabajo radica en que representa la primera reflexión sobre este tema, abriendo un campo para futuras investigaciones.
We describe a biologically inspired memory in a multi-agent based robotic architecture. In this approach, memory and pattern recognition are intertwined to form a cognitive memory that is used for recognition of objects in a robotics environment.This memory is implemented in a multiple agent behavior based blackboard architecture as an object recognition agent. The agent performance is tested against a standard dataset with satisfactory results.The system is currently installed in a mobile robotic platform where its capabilities and applications are explored.
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