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Ruiz-Mier, Sergio; Talavage, Joseph
doi: 10.1177/003754978704800404pmid: N/A
The network modeling approach to simulation provides the mo deler with simple and powerful concepts which can be used to capture the significant aspects of the system to be modeled. Yet the most advanced network modeling environments lack ex plicit concepts for the representation of complex behavior such as decision-making. Artificial intelligence research, because of its emphasis on knowledge representation, provides several meth odologies which can be successfully applied to the modeling of decision-making behavior.The approach to modeling complex behavior, outlined in this paper, is based on a hybrid methodology unifying the concepts of Object-Oriented programming, Logic Programming and the discrete event approach to systems modeling. We present SIM YON, an experimental network simulation environment which provides explicit constructs for the representation of complex behavior of real-world systems. SIMYON is implemented by de fining a library of logic objects in the Object-Oriented, Logic Programming environment CAYENE. These objects, which are analogous to the "nodes" of network simulation languages, are the building blocks for modeling.Examples are given in SIMYON to model a job scheduler in a manufacturing situation and an adaptive material handling dis patch mechanism for flexible manufacturing systems.
doi: 10.1177/003754978704800406pmid: N/A
New classes of quality assurance concepts and techniques are required for the advanced knowledge-processing paradigms (such as artificial intelligence, expert systems, or knowledge-based sys tems) and the complex problems that only simulative systems can cope with. A systematization of quality assurance problems as well as examples are given to traditional and cognizant quality assurance techniques in traditional and cognizant modelling and simulation.
Hill, Timothy R.; Roberts, Stephen D.
doi: 10.1177/003754978704800407pmid: N/A
As a preliminary step toward the goal of an intelligent automated system for simulation modeling support, we explore the feasibility of the overall concept by generating and testing a prototypical framework. A prototype knowledge-based computer system was developed to support a senior level course in industrial engineer ing so that the overall feasibility of an expert simulation support system could be studied in a controlled and observable setting. The system behavior mimics the diagnostic (intelligent) process performed by the course instructor and teaching assistants, find ing logical errors in INSIGHT simulation models and recom mending appropriate corrective measures. The system was pro grammed in a non-procedural language (PROLOG) and designed to run interactively with students working on course homework and projects. The knowledge-based structure supports intelligent behavior, providing its users with access to an evolving accumula tion of expert diagnostic knowledge. The non-procedural ap proach facilitates the maintenance of the system and helps merge the roles of expert and knowledge engineer by allowing new knowledge to be easily incorporated without regard to the ex isting flow of control. The background, features and design of the system are described and preliminary results are reported. Initial success is judged to demonstrate the utility of the reported approach and support the ultimate goal of an intelligent model ing system which can support simulation modelers outside the classroom environment. Finally, future extensions are suggested.
doi: 10.1177/003754978704800408pmid: N/A
Combining artificial intelligence concepts with traditional simula tion methodologies yields a powerful design support tool known as knowledge based simulation. This approach turns a descrip tive simulation tool into a prescriptive tool, one which recom mends specific goals. Much work in the area of general goal pro cessing and explanation of recommendations remains to be done.
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