This paper addresses the challenge of multi-policy optimization in decentralized autonomic systems. We evaluate several multi-policy reinforcement learning-based optimization techniques in an urban traffic control simulation, a canonical example of a decentralized autonomic system. Our results indicate that W-learning, which learns separately for each policy and then selects between nominated a...

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

introduction: in traditional medical education systems much interest is placed on the cramming of basic and clinical facts without considering their applicability in the future professional career. the aim of this study is to evaluate a novice medical training method (problem-based learning) as compared to the contemporary teacher-based medical education or traditional methods. methods: selecti...

with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

The present paper addresses an effective cyber defense model by applying information fusion based game theoretical approaches‎. ‎In the present paper, we are trying to improve previous models by applying stochastic optimal control and robust optimization techniques‎. ‎Jump processes are applied to model different and complex situations in cyber games‎. ‎Applying jump processes we propose some m...

Due to the uncertainty and randomness of clean energy, microgrid operation is often prone instability, which requires implementation a robust adaptive optimization scheduling method. In this paper, model-based reinforcement learning algorithm applied optimal problem microgrids. During training process, current learned networks are used assist Monte Carlo Tree Search (MCTS) in completing game hi...