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DEVELOPING SAFETY ASSESSMENT SYSTEMS USING EXPERT SYSTEM SHELLS-2

Acknowledgements: The author wants to thank XpertRule Software LTD and mr. Tim Sell for being able to try Decision Author- the main software in which this prototype shall be built.


THE DESIGN OF THE EXPERT SYSTEM

The design of the expert system starts by defining heuristic knowledge regarding the aspects that would be assessed. The primary parts are named Questions. Xpert Rule offers a variety of question types, from the simpler yes-no to more complex with multiple answers, etc.
The knowledge structured for the primary knowledge (as yes/no questions) is presented in figure 1.


Figure 4- Knowledge structured in yes/no questions


Figure 5-Primary and mid-level knowledge structured in questions


Figure 6- All the knowledge is structured here

The schema of structuring knowledge is presented in the figure  below.


Figure 7 Structuring Knowledge

THE DECISION TREE

Xpert Rules is configuring the expertise required to solve the problem around a decision tree. We have constructed our decision thee as in the figure- in order to:
-catch as much as possible of primary knowledge- that is main heuristic knowledge;
-use mid-level knowledge- that is the knowledge managed generally by supervisors and floor level managers- as needed;
-use also high level knowledge;
As our main goal is to make a risk/safety assessment- all the acquired/elicited knowledge was tailored in this respect- as seen in figure 8

Figure 8 Knowledge coupling for the building of the decision tree

Actually there are three distinct trees in the structure:
a. The Start Tree is the default Xpert Rule structure. In order to execute the expert system all the other trees must be copied/assembled into the Start Tree.
b. The EMPLOYEE_SAFETY_TREE is the main tree of this expert system- with the knowledge organised as presented before into three zones of primary, mid-level and high.
c. Tree_cont is the continuation of the main tree with the two procedures that are defined to count the Risk/Safety level and to evaluate it. This tree was developed in the idea to modularise the structure- otherwise the procedures could be assembled into the EMPLOEE tree.
Figure 9 shows the hierarchy of these trees as appears in the design window.


Figure 9 Knowledge trees

The knowledge maps of the EMPLOEE and Start trees are shown in the next figures.
Figure 10  Knowledge map of EMPLOEE_SAFETY_TREE

Figure 11 Knowledge map for the Start Tree

Structures of the primary knowledge part, of the mid- level knowledge part and of the high level knowledge part are presented in the next figures.

Figure 12 Primary Knowledge part of EMPLOYEE_SAFETY_TREE


Figure 13 Mid-level Knowledge part of the same tree


Figure 14 High level Knowledge




Figure 15 Start Tree

Here are collected some data regarding the audit- as auditor name and the place where the audit was performed.

Some aspects from a trial run of the expert system are shown in the figures below.
Figure 16 Yes/No Question


Figure 17 1-3 scale question

Figure 18 1-5 scale question

Figure 19 Partial solution

 CONCLUSIONS

 Could be such an approach helpful for the safety and health domain ? The development and upgrade of specialised expert systems could transform the process of safety assurance, especially in process industries into a  facile and efficient matter- as the existing heuristic knowledge could be elicited and added to the already existent knowledge. The development of an expert system is no more a very specific IT problem- such systems could be developed also by non- IT specialists.
We are interested to pilot our experience to interested parties.  



BIBLIOGRAPHY

Bech-Larsen, T. and Nielsen, N.A.(1999) A comparison of five elicitation techniques for elicitation of attributes of low involvement products. Journal of Economic Psychology 20, 3 (1999), 315-341

Grunert, K.G. and Bech-Larsen, T.(2005), Explaining choice option attractiveness by beliefs elicited by the laddering method. Journal of Economic Psychology 26, 2 (2005), 223-241.

Jans, G. and Calvi, L.(2006) Using Laddering and Association Techniques to Develop a User-Friendly Mobile (City) Application. In On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops. 2006, 1956-1965
Reynolds, T. and Gutman, J.(1988) Laddering theory, method, analysis, and interpretation. Journal of Advertising Research 28 (1), pp. 11-31, 1988.

Vanden Abeele, V. and Zaman, B.(2008), The Extended Likeability Framework: A Theoretical Framework for and a Practical Case of Designing Likeable Media Applications for Pre-schoolers. Advances in Human Computer Interaction 2008

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