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This paper presents an approach to agent's action selection strategy called stepwise case-based reasoning (SCBR). In this paper an agent that roams in 3D synthetic world is called an SCBR agent. An SCBR agent is an entity that selects the next action based on previous interaction experience and on simulated vision. An SCBR agent's interaction experience is represented in the form of two different types of cases: plan cases and contextual cases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple and efficient method to construct an accurate fuzzy classification system. In order to optimize the generalization accuracy, we use rule-weight as a simple mechanism to tune the classifier and propose a new learning method to iteratively adjust the weight of fuzzy rules. The rule-weights in the proposed method are derived by solving the minimization problem through gradient descent. Through computer simulations on some data sets from UCI repository, the proposed scheme shows a uniformly good behavior and achieves results which are comparable or better than other fuzzy and non-fuzzy classification systems, proposed in the past. [ABSTRACT FROM AUTHOR]