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This paper presents a new kind of learning algorithm to help agents learn sensorimotor skills through their contact with the environment. The learning mechanism is based on Skinner's theory of operant conditioning (OC), which is improving the probabilities of good actions, and reducing the probabilities of bad actions. The concept of curiosity is introduced as the intrinsic motivation for an agent...
We investigate a relay network where the source and destination select one relay out of a group of untrusted relay nodes to establish a reliable and confidential connection. We assume there is no direct link between them, and the users have to employ an untrusted relay while simultaneously protecting the confidential data from it. We study joint transmit design and node selection strategies for both...
Attribute reduction methods of Rough set and parallel computation are analyzed in this paper, then cooperating the distributed computational theory, a high-efficient parallel and distributed attribute reduction method for rough set is proposed. The algorithm consist of expansion and contraction stages, which on condition that the reduction results is Pawlak reduction, the algorithm dispatch the attribute...
A general framework for sequential particle filtering on graphs is presented in this paper. We present two new articulated motion analysis and object tracking approaches: the graph-based sequential particle filtering framework for articulated object tracking and its hierarchical counterpart. Specifically, we estimate the interaction density by an efficient decomposed inter-part interaction model....
Data mining is a strategic tool that is very difficult for common users to do without special training. Costs will be great to employ specialized people to do the data mining work. In order to resolve this problem, this paper proposes a framework of an automated data mining system based on intelligent agents.
In order to improve the face recognition rate under complex light conditions and address the phenomenon such as white and color distortion, low contrast, which might be produced while using retinex algorithm to process face images, this paper proposes a new and multi-scale retinex with color restoration algorithm for light compensation of color images. On the basis of studying of MSRCR theory, we...
In CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. Aiming at such weakness of CBR system, this article proposes a novel case retrieval method based on Hybrid Ant-Fish Clustering Algorithm (HA-FC). At beginning of algorithm, we get rough cluster sets utilizing the advantage...
Research has shown that many social networks come into being hierarchically based on some basic building blocks called communities, within which the social interactions are very intensive, but between which they are very weak. Network community mining algorithms aim at efficiently and effectively discovering all such communities from a given network. Many related methods have been proposed and applied...
Recent years, the artificial neural networks were noticeable on development. As the bridge between the theoretical research and application research, the hardware implementation technologies have developed rapidly, particularly in configurable FPGA implementation technologies. However we have found the shortcomings of the existing methods which need to be improved. This paper has taken research on...
Realizing AES in hardware faces increasingly more stringent demands for low cost as well as resisting power attacks. For security consideration, countermeasure power analysis approaches to mask sensitive data are needed. The algebraic masking method to protect AES against power attacks is based on various representations of underlying finite fields. However, implementing the transfer matrices between...
Q-learning is an effective model-free reinforcement learning algorithm. However, Q-learning is centralized and competent only for single agent learning but not multi-agent learning because in later case the size of state-action space is huge and will grow exponentially with the number of agents increasing. In the paper we present a distributed Q-learning algorithm to solving this problem. In our algorithm,...
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