Anul 2017 a fost într-un anumit sens, anul marilor așteptări- așteptări care sperăm să înceapă să se concretizeze în 2018.
În acest an am continuat participarea la proiectul ”Safety matters ! Mind your safety” cu realizarea următoarelor:
-1. adaptarea și traducerea rezultatului O1- rezultat care s-a concretizat într-un ghid pentru profesorii și formatorii în domeniul Securității și Sănătății în Muncă- în special pentru tinerii cu vârste între 14-18 ani;
-2. realizarea unor hărți de cunoaștere combinate cu planuri de lecție- pentru aceeași categorie de vârstă;
-3. traducere și voice-over pentru un material video realizat de către universitatea din Aveiro.
Specialiștii din cadrul INCDPM ”Alexandru Darabont”, respectiv dna. Alina Trifu și dl. ștefan Kovacs au participat la evenimentul multi-tasking realizat în cadrul acestui proiect la Praga- în luna Septembrie 2017, eveniment coordonat de către colegii praghezi din cadrul VUBP.
S-au discutat pregătirile pentru ședința de închidere a proiectului - care va avea loc în luna iunie 2018 la Lisabona- printr-un eveniment care va angrena peste 200 de cadre didactice din Portugalia și Spania.
La Mulți Ani 2018 !
vineri, 29 decembrie 2017
duminică, 22 octombrie 2017
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
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
DEVELOPING SAFETY ASSESSMENT SYSTEMS USING EXPERT SYSTEM SHELLS-1
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.
GENERAL ASPECTS
Safety domain of research is by excellence a domain based on
expertise. Textbooks and theoretical knowledge are good but the safety expert
which inspects three times a day a certain part of an enterprise is the
ultimate safety dealer here.
A lot of expertise is transformed into lessons learned- that
are used for training and improvement of existing safety attitudes. On the
other part, this expertise could be also valued in order to build optimal and
effective safety assessment systems.
An expert system is software that emulates the
decision-making ability of a human expert. In our case- the expert part should interrogate
the specific employees regarding safety aspects of an enterprise.
The next figure illustrates how a safety expert, with the
necessary knowledge into the problem could improve a safety assessment process.
The safety manager of a specific enterprise which performs a safety assessment
will navigate through the safety assessment items, will assess something (the
current safety situation inside his enterprise- preferably) and will develop a
safety improvement plan- this being the outcome of the assessment. of course
that this assessment could be external and then we will have third party auditors-
but essentially the process is the same. We need from our safety expert:
-to design optimally and efficiently the safety assessment
items- so that the answers of the safety manager should capture the real, most
significant and important from the safety point of view aspects;
-to develop the objective and optimal safety assessment
procedure so that a very subjective process should perform as objective as
possible;
-to build a reliable and cost sensitive improvement plan
that will do exactly this- to improve safety and health on the basis of the
performed safety assessment.
Generally, the existing safety assessment methods are using
textbook based evaluation items- in order to assure a very large usage
possibility. This is good- on one part- because they are giving us a certain
referential that is the same in USA, France or Romania. This is bad, on another
part- because it is not using the existing safety expertise in defining and
developing the safety assessment items.
In our paper we are proposing an original approach regarding
the development of optimal safety assessment solutions using expert system
developments.
Figure 1 Safety Expert
helps Safety Manager to perform an objective assessment
Safety Knowledge Elicitation
Ok, we have tons of safety knowledge. Do we need more? What
is in into knowledge elicitation? Expertise is a collection of knowledge,
experience, skills, techniques, facts, rules and so on, and using them to lead
to goals. This is also valuable for safety expertise. We must say that expertise
is unique reported to the context, to the domain and also to the place where
expertise is established. The knowledge is elicited chiefly from experts in the
field and data/ information available from published literature. There are
various known knowledge elicitation techniques available. The choice of
technique to be used in the knowledge elicitation process depends on the nature
of the situation within which the knowledge is elicited, the domain knowledge
and availability of experts. Elicitation could be defined as the process of
acquiring knowledge from a domain expert- with some specific problems. The
knowledge engineer (Bech-Larsen, 1999) will find it difficult to structure the
interviews. On one hand interrupting the expert may break his train of thought
and lose useful information; on the other hand, along digression into anecdotes
or irrelevant information will make the transcript difficult to understand.
However, it is hard to judge what is irrelevant, or where the expert train of
thought is leading. The problems are compounded by the shades of colour in
language. The expert will use technical terms, and the knowledge engineer must
make sure that he and the expert have the same understanding of this
terminology. The words such as big, small, frequently, sometimes are fuzzy, it
is difficult to define. Also, the definition may depend on the context. It may
be that the expert himself is not even consistent in his use of words (Vanden,
2008).
Laddering (Reynolds, 1988) is a structured questioning technique
derived from the repertory grid technique, a, specific knowledge elicitation
method that could apply well into safety. In Laddering, a hierarchical
structure of the domain is formed by asking questions designed to move up,
down, and across the hierarchy... Laddering is enabling a hierarchy of concepts
to be established. The common generic means-end chain, therefore, consists of
Safety Attributes (SA), Safety Consequences (C) and Safety Added Value (SAV).
Safety Attributes -> Safety Consequences -> Safety
Added Value
Laddering (Grunnert, 2005) allows crossing over from
qualitative data gathering to quantitative data treatment. The laddering data
analysis process (Jans 2006) typically involves the following two phases.
First, the laddering interviews are transcribed and core elements -attributes,
consequences, and values- are coded. This phase relies on the qualitative
research tradition, requiring skills such as axial coding to define elements
that emerge from the interviews. Once that the core elements are defined and labelled,
the individual ladders can be decomposed based on these codes.
Laddering is illustrated in the next figure.
Figure 2 Laddering as
safety knowledge elicitation technique
20 Questions is a method used to determine how the expert
gathers information by having the expert as the knowledge engineer questions-
having as output a. amount and type of information used to solve problems; how
problem space is organized, or how expert has represented task-relevant
knowledge.
In our research we have built a questionnaire for
identifying, capturing and using heuristic knowledge, considering that we could
add significant amount of knowledge to the existing one in the already done
assessment. An example is given below.
New Knowledge elicited
|
Elicitation trigger
|
Former Knowledge
|
You must check especially if a
maintenance job is repetitive done on the pipes that are alimenting processor
X. Maintenance teams are usually solving punctual problems- without checking
the safety of the whole structure. Be especially careful if they used to heat
the pipes in order to make the maintenance.
|
From your experience- could you tell us something that
could jeopardise the safety regarding alimentation pipes for processor X?
|
Alimentation pipes shall be
checked.
|
Decision Author is well suited in order to
introduce”Knowledge on the fly” into the expert structure, knowledge derived
from elicitation.
The elicitation process is presented in detail in the next
figure.
Figure 3 Elicitation
process
To be continued...
To be continued...
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