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In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for developing intelligent agents capable of adapting to the dynamic environment of multiagent systems (MASs). Specifically, we take inspiration from Darwin’s theory of natural selection and Universal Darwinism as the principal driving forces that govern the evolutionary knowledge transfer process. The essential...
An improved manifold learning method, called enhanced semi-supervised local fisher discriminant analysis (ESELF), for gene expression data classification is proposed. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The...
The incorporation of intelligent agents into the realms of Virtual Environments (VE) has demonstrated a high potential in improving user interactivity, participation and satisfaction in these virtual worlds. In order to stay relevant to the demands and expectations of users in a Virtual Environment, agents must be able to adapt quickly and learn from other agents in order to perform well. The novelty...
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