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[24]IBM Intelligent Agent Center, IBM Agent Building Environment DeveloperToolkit, Level 5, 1997. 博士從香港大學(xué)獲得理學(xué)士學(xué)位，并且在美國的伯明翰大學(xué)獲得理學(xué)碩士和博士學(xué)位。 A cooperative agent modelling approach for process planning . Zhao, . Tso ), Paul . Wu Department of Manufacturing Engineering and Engineering Management, City Uni205。 Cooperative agent。 175。 175。 175。 Interoperability permits multiple heterogeneous machines or approaches to work smoothly together in solving problems. 175。 175。 175。 175。 175。 175。 Scalability offers the ability to scale the CAPP system architecture according to the user’s transaction requirements. The design of the CoCAPP framework is discussed with relation to a machining process planning(Machining CoCAPP). Its structure, implementation, and its application case study are detailed in this paper. . Overview of Machining CoCAPP The overall structure of the Machining CoCAPP system in the integration environment of CAD/CAPP/CAM is shown in Fig. 1. The following six process planning agents (Pagents)are in general included in the implementation. 175。 Autonomy means that the CAPP system is developed as independent system. Once developed, it can readily be integrated into the CAD/CAM system. Each agent is also treated as an independent and autonomous system. 175。 175。 175。 CIM 1. Introduction Process planning provides information to the shopfloor on how to produce the designed products. It addresses each part of the product separately and collectively. It defines the process, cost and production lead time under the constraints such as the designed geometry, material, quantity, machine and tooling availability, labour capacity and suitability, Corresponding author. etc. In the past, process plans were often generated by human process planners who had plenty of manufacturing domain knowledge and worthy the recent decades, puter technologies have stimulated the advance toward puter aided process planning(CAPP). Generally, there are two CAPP approaches: variant and generative. The variant approach is a data retrieval and editing method. Some standard or mature process plans are collected based on the group technology and stored in a database. When a new part is required to be produced, a similar process plan is retrieved from the database and edited to adjust it to suit the new part. The generative approach is a knowledgebased method which automatically generates a process plan according to the part’s features and manufacturing requirements. The success of the variant approach depends on the group technology, planner’s experience and a sufficient collection of standard or mature process plans. This method is especially suitable for panies with few product families and a large number of parts per family. Most earlier CAPP tools can be categorized under the variant process planning approachw1x. Typical examples are CAPP [2], MIPLAN[3], etc. The generative process planning approach has attracted more attention in recent years. It offers a potential of producing an optimal examples are APPAS [2] EXCAP [4], KRONOS [5], XCUT [6], QTC(Quick turnaround cell)[2], PART [7], OOPPS (objectoriented process planning system)[8], MePlans[9], COMPLAN Process Planner (CPP)[10], etc. Generative process planning systems are mostly oriented towards the needs of large panies and research organizations, especially those which have a number of products in small lot sizes. However, there is still difficulty in developing a truly generative process planning system which can meet industrial needs and provide an appropriate and patible generic framework, knowledge representation method, and inference mechanism. Cooperative agent systems attempt to distribute activities to multiple specialized problem solvers and to coordinate them to solve plex problems [11–14]. A cooperative agent system consists of many individual agents with cooperation engines. Each agent which has its own knowledge base and inferenc