Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Soft computing techniques for the design of mobile robot behaviors
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
An Behavior-based Robotics
An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Distance-based dynamic interaction of humanoid robot with multiple people
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Robot competition using gesture based interface
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Evolutionary behavior learning for action-based environment modeling by a mobile robot
Applied Soft Computing
Learning performance in evolutionary behavior based mobile robot navigation
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Dynamic penalty based GA for inducing fuzzy inference systems
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Extending adaptive fuzzy behavior hierarchies to multiple levels of composite behaviors
Robotics and Autonomous Systems
Real-Time adaptive fuzzy motivations for evolutionary behavior learning by a mobile robot
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based navigation and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique is shown as a simple and powerful method for a robot to acquire a diverse set of fit behaviors as well as providing an intuitive user interface framework.