Seyed Hossein Khasteh

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In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-single output systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning(More)
One of the most important issues discussed in multi-agent systems, is communication between agents. Usually, a predefined communication protocol is used, but predefining a protocol between agents causes some problems; for example it is possible that agents recognize a subject that is not defined in their protocol so they can't express anything about it. In(More)
Active Learning Method (ALM) is a soft computing method used for modeling and control based on fuzzy logic. All operators defined for fuzzy sets must serve as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling method, ALM does not possess operators which serve as S-norms and T-norms which deprive it of a profound analytical(More)
Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic. Although ALM has shown that it acts well in dynamic environments, its operators cannot support it very well in complex situations due to losing data. Thus ALM can find better membership functions if more appropriate operators be chosen for(More)