The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.

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The design of a grinding process is a di cult task since there are so many characteristics to consider. In this study, a generic scheme to establish the norm for automation of design by employing fuzzy logic and neural networks for a surface grinding process is proposed. Design of a grinding process is accomplished by initial determination of a set of optimal design variables in order to achieve a set of desired process variables. First, the important features of a surface grinding process are identi® ed. Next, advisory systems for surface grinding design are reviewed. After that, a`fuzzy grinding optimizer' (FGO) and a`neural grinding optimizer' (NGO) are proposed. In addition, a generic scheme called`bi-directional construction of fuzzy and neural systems' (BCFNS) is proposed for performance evaluation and comparison between fuzzy logic and neural networks. Finally, future research directions are pointed out concerning performance evaluation for various types of grinding optimizers.

Based on theoretical results, fuzzy systems are universal approximators. In this paper, we propose a novel learning approach, self-organizing and selfadjusting fuzzy modeling (SOS4 FM), for inference rules. Basically, the proposed system consists of two stages, the self-organizing state (SOS) and the selfadjusting stage (SAS). In the $rst stage, the input data is divided into several groups by applying Kohonen's feature maps. Gaussian distribution functions are employed as the standard form of the membership functions. Methods of statistics are used to determine the center and width of the membership function for each group. Regarding the consequences, the linear regression method is used. AjZer the above procedures, we can decide the initial parameters of fuzzy system.Then, the error backpropagation-type learning method is used to $ne-tune the parameters. The simulation results show that the proposed approach is better than conventional neural networks in both accuracy and speed.