In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learni...In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.展开更多
In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of...In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.展开更多
The purpose of objective and quantitative evaluating the organizational learning ability is to make the enterprise have reliable criterions and operable procedures during the process of improving the organizational le...The purpose of objective and quantitative evaluating the organizational learning ability is to make the enterprise have reliable criterions and operable procedures during the process of improving the organizational learning ability and founding learning organization. In this paper, an evaluating indicator system is established by analyzing the whole process of organizational learning; both qualitative and quantitative analyses are used, and a new combined evaluating method is presented. A measurement case involving twenty enterprises is given to prove the validity of this method. In conclusion, learning strategies are presented according to measurement result. This paper applies 4 models synthetically. The evaluating effect is superior to other methods.展开更多
文摘In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.
基金Project supported by the National Natural Science Foundation of China (Nos. 60874098 and 60911130129)the High-Tech Research and Development Program (863) of China (No. 2007AA042103)+1 种基金the National Creative Research Groups Science Foundation of China (No. 60721062)the Project of Introducing Talents for Chinese University Disciplinal Innovation (111 Project, No. B07031)
文摘In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.
文摘The purpose of objective and quantitative evaluating the organizational learning ability is to make the enterprise have reliable criterions and operable procedures during the process of improving the organizational learning ability and founding learning organization. In this paper, an evaluating indicator system is established by analyzing the whole process of organizational learning; both qualitative and quantitative analyses are used, and a new combined evaluating method is presented. A measurement case involving twenty enterprises is given to prove the validity of this method. In conclusion, learning strategies are presented according to measurement result. This paper applies 4 models synthetically. The evaluating effect is superior to other methods.
