The role of artifical intelligence in discrete-event simulation
Artificial intelligence, simulation & modeling
Virtual reality and simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
Proceedings of the 29th conference on Winter simulation
An expert systems approach to simulating the human decision maker
Proceedings of the 30th conference on Winter simulation
Virtual worlds: experiencing virtual factories of the future
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Integrating operations simulation results with an immersive virtual reality environment
WSC '04 Proceedings of the 36th conference on Winter simulation
Experimental investigation of the impacts of virtual reality on discrete-event simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Practitioners' perception of the impacts of virtual reality on discrete-event simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Knowledge based decision making on higher level strategic concerns: system dynamics approach
Expert Systems with Applications: An International Journal
Design of a PCB plant with expert system and simulation approach
Expert Systems with Applications: An International Journal
International Journal of Geographical Information Science - Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Since much knowledge is tacit, eliciting knowledge is a common bottleneck during the development of knowledge-based systems. Visual interactive simulation (VIS) has been proposed as a means for eliciting experts' decision-making by getting them to interact with a visual simulation of the real system in which they work. In order to explore the effectiveness and efficiency of VIS based knowledge elicitation, an experiment has been carried out with decision-makers in a Ford Motor Company engine assembly plant. The model properties under investigation were the level of visual representation (2-dimensional, 21/2-dimensional and 3-dimensional) and the model parameter settings (unadjusted and adjusted to represent more uncommon and extreme situations). The conclusion from the experiment is that using a 2-dimensional representation with adjusted parameter settings provides the better simulation-based means for eliciting knowledge, at least for the case modelled.