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This paper concerns about a way of intellectualization of robots (called "agent" here). Human learns incidents by own actions and reflects them on the subsequent actions as own experiences. These experiences are memorized in his/her brain and recollected and reused if necessary. This research incorporates such an intelligent information processing mechanism, and applies it to an autonomous...
Learning and relearning ability is important to partner robots. From this point of view, here we propose an intelligent learning system with voice instruction recognition and action learning function. Transient-SOM (T-SOM), an advanced self-organizing map proposed by our previous work for hand gesture recognition and memorization is adopted and improved to be Parameter-Less T-SOM (PL-T-SOM) with an...
This paper proposes the system that combines self-organized fuzzy-neural networks with reinforcement learning system (Q-learning, stochastic gradient ascent : SGA) to realize the autonomous robot behavior learning for continuous state space. The self-organized fuzzy neural network works as adaptive input state space classifier to adapt the change of environment, the part of reinforcement learning...
Human learns incidents by own actions and reflects them on the subsequent action as own experiences. These experiences are memorized in his brain and recollected if necessary. This research incorporates such an intelligent information processing mechanism, and applies it to an autonomous agent that has three main functions: learning, memorization and associative recollection. In the proposed system,...
This paper proposes a neuro-fuzzy system with a reinforcement learning algorithm to realize speedy acquisition of optimal swarm behaviors. The proposed system is constructed with a part of input states classification by the fuzzy net and a part of optimal behavior learning network adopting the actor-critic method. The membership functions and fuzzy rules in the fuzzy net are adaptively formed online...
The present paper proposes an objective-based reinforcement learning system for multiple autonomous mobile robots to acquire cooperative behavior. The proposed system employs profit sharing (PS) as a learning method. A major characteristic of the system is using two kinds of PS tables. One is to learn cooperative behavior using information on other agents' positions and the other is to learn how to...
Feeling and emotion are important to human being during his/her learning process, also valuable to be adopted into intelligent machines. This research presents a system which forms and expresses feelings of a robot. The vision information of robot is used and the environment features are categorized by a hierarchical SOM (Self-Organization Map). The proposed SOM here combines a feature map, an action...
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