Autonomous Robots
An overview of distributed artificial intelligence
Foundations of distributed artificial intelligence
Applications of distributed artificial intelligence in industry
Foundations of distributed artificial intelligence
Reactive distributed artificial intelligence: principles and applications
Foundations of distributed artificial intelligence
IMAGINE: an integrated environment for constructing distributed artificial intelligence systems
Foundations of distributed artificial intelligence
AGenDA—a general testbed for distributed artificial intelligence applications
Foundations of distributed artificial intelligence
Application of Neural Networks to Adaptive Control of Nonlinear Systems
Application of Neural Networks to Adaptive Control of Nonlinear Systems
Superposed Generalized Stochastic Petri Nets: Definition and Efficient Solution
Proceedings of the 15th International Conference on Application and Theory of Petri Nets
A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots
Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots
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In this paper fhe problem of motion control of a biped is considered. We develop a new method based on multi-agent associated Neural AIGLS (On-line Augmented Integration of Gradient and Last Sguare method) – RSPN (Recursive Stochastic Petri Nets) strategy. This method deals with organization and coordination aspects in an intelligent modeling of human motion. We propose a cooperative multi-agent model. Based on this model, we develop a control kernel named IMCOK (Intelligent Motion COntrol Kernel) which consists of a controller, a coordinator and an executor of different cycles of the motion of the biped. When walking, IMCOK receives messages and sends offers. A Decision Making of Actions (DMA) is developed at the supervisor level. The articulator agents partially planify the motion of the associated non-articulator agents. The system is hybrid and distributed functionally. The learning of the biped is performed using an On-line Augmented Integration of Gradient and Last Sguare Neural Networks based algorithm. In the conflictual situations of sending or receiving messages by the managers of MABS we apply a new strategy: Recursive Stochastic Petri Nets (RSPN). This module is fundamental in the On-line information processing between agents. It allows particularly the Recursive strategy concept. Cognitive agents communicate with reactive (non-articulator) agents in order to generate the motion.