A quest for a better simulation-based knowledge elicitation tool

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Abstract

Knowledge elicitation is a well-known bottleneck in the development of Knowledge-Based Systems (KBS). This is mainly due to the tacit property of knowledge, which renders it unfriendly for explication and therefore, analysis. Previous research shows that Visual Interactive Simulation (VIS) can be used to elicit episodic knowledge in the form of example cases of decisions from the decision makers for machine learning purposes, with a view to building a KBS subsequently. Notwithstanding, there are still issues that need to be explored; these include how to make a better use of existing commercial off-the-shelf VIS packages in order to improve the knowledge elicitation process' effectiveness and efficiency.

Based in a Ford Motor Company (Ford) engine assembly plant in Dagenham (East London), an experiment was planned and performed to investigate the effects of using various VIS models with different levels of visual fidelity and settings on the elicitation process. The empirical work that was carried out can be grouped broadly into eight activities, which began with gaining an understanding of the case study. Next, it was followed by four concurrent activities of designing the experiment, adapting a current VIS model provided by Ford to support a gaming mode and then assessing it, and devising the meaures for evaluating the elicitation process. Following these, eight Ford personnel, who are proficient decision makers in the simulated operations system, were organised to play with the game models in 48 knowledge elicitation sessions over 19 weeks. In so doing, example cases were collected during the personnel's interactions with the game models. Lastly, the example cases were processed and analysed, and the findings were discussed.

Eventually, it seems that the decisions elicited through a 2-Dimensional (2D) VIS model are probably more realistic than those elicited through other equivalent models with a higher level of visual fidelity. Moreover, the former also emerges to be a more efficient knowledge elicitation tool. In addition, it appears that the decisions elicited through a VIS model that is adjusted to simulate more uncommon and extreme scenes are made for a wider range of situations. Consequently, it can be concluded that using a 2D VIS model that has been adjusted to simulate more uncommon and extreme situations is the optimal VIS-based means for eliciting episodic knowledge.

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