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Researchers are finding their way to solve the chaotic and uncertain problems using the extensions of classical fuzzy model. At present Interval Type-2 Fuzzy logic Systems (IT2-FLS) are extensively used after the thriving exploitation of Type-2 FLS. Fuzzy time series models have been used for forecasting stock and FOREX indexes, enrollments, temperature, disease diagnosing and weather. In this paper an integrated fuzzy time series model based on ARIMA and IT2-FLS is presented. The propose model will use ARIMA to select appropriate coefficients from the observed dataset. IT2-FLS is utilized here for forecasting the result with more accuracy and certainty. [ABSTRACT FROM AUTHOR]

In this paper, we present a solution for autonomous agent navigation in unknown virtual environments. A new decision making method using fuzzy logic is proposed. The objective is to solve behaviour conflicts in behaviour-based architectures. Two main problems have been identified: how to decide which behaviour should be activated at each instant; and how to combine the results from different behaviours into one action. The method uses fuzzy _-levels to compute behaviour weights for each behaviour and the final action is selected using the Hurwicz criterion. The experimental results show that the autonomous virtual agent had successfully navigated various virtual environments. The results show the virtual agent required less decision making which means more reliable decisions had been generated and redundant decisions had been reduced. The qualities of the path produced are reasonably smooth, short and clear of collision. [ABSTRACT FROM AUTHOR]