Knowledge based systems have emerged as an efficient solution for complex problems in many areas, including manufacturing and engineering. There are various situations, however, where neither symbolic procedures nor algorithmic routines can alone provide the answer. A balanced mixture of causal and heuristic processing modules offers a more efficient and comprehensive approach in complex cases. The different architecture types using the combination of these techniques are known as knowledge based hybrid systems (KBHS). This volume presents a comprehensive discussion of the results achieved using knowledge based hybrid systems (KBHS). The papers, both practical and theoretical, are divided into seven major sections, each covering a separate topic: hybrid systems for CIM applications; knowledge based product design; coupling learning with other paradigms; problems of manufacturing automation; hybrid systems in scheduling; knowledge based simulation; and hybrid techniques in monitoring and maintenance.
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Knowledge based systems have emerged as an efficient solution for complex problems in many areas, including manufacturing and engineering. There are various situations, however, where neither symbolic procedures nor algorithmic routines can alone provide the answer. A balanced mixture of causal and heuristic processing modules offers a more efficient and comprehensive approach in complex cases. The different architecture types using the combination of these techniques are known as knowledge based hybrid systems (KBHS). This volume presents a comprehensive discussion of the results achieved using knowledge based hybrid systems (KBHS). The papers, both practical and theoretical, are divided into seven major sections, each covering a separate topic: hybrid systems for CIM applications; knowledge based product design; coupling learning with other paradigms; problems of manufacturing automation; hybrid systems in scheduling; knowledge based simulation; and hybrid techniques in monitoring and maintenance.
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