Qualitative modelling, a concept for hoss design a preliminary study

/ Ith European Arnual Conference on Human Décision Making and Manual Control

Q U A L I T A T I V E M O D E L L I N G , A C O N C E P T F O R H O S S D E S I G N ,

A P R E L I M I N A R Y S T U D Y

Diederik P. Wawoe

Peter A. Wieringa

Man-Machine Systems and Control, Faculty of Design, Engineering and Production,

Delft University of Technology

Mekelweg2, 2628 CD Delft, The Netherlands

D.P. WAWOE@wbmt.tudelft.nl

P.A.WIERINGA@wbmt.tudelft.nl

A B S T R A C T

It is generally accepted that a human Operator uses some kind of représentation of a plant. There are several reasons for using a qualitative model as a concept for human Operator support System (HOSS) design: 1. Qualitative models are seen as common sense models by several authors,

2. Qualitative models prove to be important for

experts that try to solve a physics problem and 3. Qualitative models are easier to build than quantitative models. It is important to know what fraction of the représentation of a plant is qualitative by nature. If this représentation is indeed as important as we think, it should be used as a concept for HOSS design. In an experiment, we let 3 Student Operators make a verbal protocol while they handled alarms in a simulated plant. The Statements made by thèse students indicate that their représentations of the processes in the simulated plant are qualitative. In another experiment, we used qualitative représentations of the heat balance of a 4 - tank System. The results showed that the specific représentation in the interface we used, was understandable but not ideal according to the subjects. So a qualitative représentation was seen as important by the students Controlling a simulated plant. The display of the qualitative model must be adapted to the operator's représentation. The display we developed showed that not just any graphical layout can be chosen.

K E Y W O R D S

Qualitative reasoning, human Operator support Systems, mental models.

I N T R O D U C T I O N

Chemical process Operators do not have detailed knowledge of ail processes they supervise. These processes are so complex that they only have a detailed knowledge of some Subsystems and a very global view of the System as a whole [13]. Also the knowledge of a plant is not universal for all Operators. This we see when a new Operator team takes over the control of a plant; set points are often changed sometimes resulting in an improvement of the plant's performance.

So the représentations of a human Operator are not always detailed, could be improved and this improvement could enhance plant performance.

In this paper we try to find out how important the qualitative model is as a part of the entire set of représentations the Operator has of a plant. There are several reasons why we focus on this qualitative model:

• The qualitative model is seen as a common sense model by several authors. It is a down to earth way to understand how a physical System works. A prove for this Statement is found by thèse authors in the fact that people fonction well within the world without knowing the exact quantitative descriptions of the physical phenomena they encounter in everyday life. Instead they make a qualitative description of every day processes such as cooking and driving a car

([2], [6]).

• The qualitative model is seen as very important for experts solving a physics problem. Experimental data proves that physics students first try to describe a problem qualitatively before they make use of a differential equation. A novice, in contrast, will jump to the differential equation almost immediately. It goes without saying that the expert outperforms the novice in solving the problem ([5], [3]). In this case the qualitative or common sense approach solves the problem faster than a rule (the differential equation) to solve the problem. This is due to the fact that the problem is new and the problem solver first has to have a clear picture of what the situation is.

• Qualitative models are easier to build than quantitative models. Qualitative approaches require less knowledge of a subsystem itself. The variables determining the behaviour of the subsystem can only take three qualitative values (e.g. "negative", "zero" and "positive" or "down", "zero" and "up").

The fact that operator representations of plants can be improved, calls for intelligent operator support. We know that if we support the human operator with advises he does not understand that he will not be able to check these advises [11]. If the qualitative model is indeed a natural or common sense model, we want to let it serve as a concept human operator support systems (ROSS) design.

T H E O R Y O N M E N T A L M O

-D E L S A N -D Q U A L I T A T I V E

M O D E L L I N G

In Rasmussen [10] three levels of human behaviour are distinguished: skill-based behaviour (i.e. behaviour taking place without conscious control), rule-based behaviour (i.e. behaviour for which the operator has a stored rule of the form "If....then..."), and knowledge based behaviour (i.e. behaviour for which there exist no predefmed rules and for which a representation of the system is required).

In this article we use the term qualitative model ([1]) for an approach in which a (differential) equation is reduced to a qualitative equation in which the variables can only have three states

(such as "positive", "zero" and "negative") and in which the equation itself is written down as a set of "if... then"-rules.

E.g. the equation X+Y=Z. Is reduced to x,y,z e {"négative", "zero", "positive"} and the set of "If....then"- rules

1. If x ^"positive" and y= "positive" then z="positive"

2. If x ="negative" and y= "negative" then z="negative"

3. If x ="zero" and y= "zero" then z="zero"

4. If x ="positive" and y= "zero" then z="positive"

5. If x ="zero" and y= "positive" then z="positive"

6. If x ="zero" and y= "negative" then z="negative"

7. If x ="negative" and y= "zero" then z="negative"

8. If x ="positive" and y= "negative" then z="?"

9. If x ="negative" and y= "positive" then z="?"

We see in the last two rules that we cannot always draw a conclusion in a qualitative model. This is one of the negative aspects of qualitative models.

Although we use a set of rules to détermine the value on a variable, we must not mistake qualitative models for models of rule based behaviour. This because the starting point of qualitative modelling is the qualitative equation which is a description of the system as it works. This means it is based on knowledge of the system.

If the operator of a chemical plant is confronted with a problem and he has not yet solved this or a similar problem, he has to rely on his knowledge of the plant. His problem solving behaviour will then generally be on the knowledge based level [13]. This type of knowledge based problem solving behaviour we want to investigate in this article. We want to make a model of it (a so

17th European Annual Conference on Human Décision Making and Manual Control

called mentar-model) and we want use this model for HOSS design.

H Y P O T H E S I S

We think that the first step an operator takes in understanding a physical phenomenon and solving knowledge based problems for this phenomenon is making a qualitative représentation of that phenomenon. An example of such a représentation is: "If I switch on the pump I will flll the tank." After some expérience with the System at hand, the operator tries to make a fuzzy model, this means he distinguishes various qualitative states and defines degrees of "on" e.g. "half speed" "quarter speed" and "to the limit" for the pump and knows that the filling of the tank will go "fast", "faster" and "very fast" respectively. If the operator is very experienced with the System he makes a quantitative model of the plant. Then he knows what flow he needs to fill a certain tank at a certain speed. (The hypothesis is shown is Figure I).

stage 1 a qualitative understanding stage 2 ftzzifîcation of input-output behaviour stage j quantification of input-ouput behaviour

This hypothesis is also based on the assumption of workers in the field of qualitative reasoning [1], who describe the qualitative model as a common sensé model.

First we have set up an experiment for students to test our hypothesis. In the future we want to use this same setting for real Operators or Student Operators.

E X P E R I M E N T 1 W I T H

L I Q U I D - V E S S E L S Y S T E M

A simulation of a liquid-vessel System

d(L) = Fj(t)-F0(t) dt A d(T) ^ 5di(t)-T(t))-F0(t) dt AL(t) Q(t) pCDAL(t) Hl [2]

Figure 1 : Hypothesis ofthree stages in understanding a physical phenomenon

L= level (m) F;= flow-in (m7 s), F0= flow-out

(m3/s), A= cross section of Tank (m2), T;=

temperature-in (K), T= température in Tank (K), Q= heat-flow (J/s), p = density (kg/m3), Cp=

specific heat (J/kg, K)

This hypothesis is based on [12] who also distinguishes between qualitative and quantitative descriptions and who describes the fuzzy description as a type of a quantitative description.

Figure 2 : The

The process

variables can be seen next to the components

and the alarm limits can be seen in the left hand

corner of the screen.

subjects

All subjects were students of the départaient of control engineering of the faculty of mechanical engineering of Delft University of Technology. (Ages between 22 and 26 years).

training

The students were sufficienfiy trained to be able to handle alarms. (Alarm handling meant acknowledgement of the alarm in the alarm-log and control action in the plant. The control action was increasing or diminishing the heat-flow or turning up or down the pump-flow by fixed amounts.)

verbal protocols

Verbal protocols are verbalisations by operators while they are carrying out a task, in the form of a commentary about their immédiate perceptions of the reasons behind them. (A description of this technique is found in [4]. If this technique is used as a way to détermine a mental model then an exact mapping from the mental processes to the verbalisation is assumed.

In our experiment we let the student operators talk while they handled alarms in our

liquid-vessel System. The alarms were -generated through step disturbances upon the heat-flow and pump-flow. Because the dynamics of the plant were simulated, the operators sometimes generated alarms themselves as a conséquence of a control action that got out of hand. The alarms were and high-temperature alarms and low-and high-level alarms of the 4 tanks low-and trips of the pumps. A trip occurred after a very low level in a tank.

results of experiment 1

The students appeared not to use a quantitative input-output model. There behaviour was qualitative trial and error alarm handling behaviour. Meaning they used qualitative statements for their observations of (level and température) variables and for their control actions (upon heaters and pumps). If, after a control action, the controlled level or température variable would not display expected behaviour, or rather if the controlled level or température would not diminish or increase with a desired amount, the student would change the input variable to a new value.

An example of this qualitative trial and error behaviour can be seen from the following statement by subject 3 during the experiment:

"Tank-3-E High-temperature,

acknowledge, heater,-10000, was 460 K,

now I make it -1000....

now I make it 0, the alarm is gone. "

We see that the subject perceives a high température alarm in Tank-3-E, acknowledges it, goes to the heater of the tank, controls the heater by -10000 and perceives that the température is 460 K, waits a while, readapts the heater to -1000, sets the heater to 0 and sees that the alarm is gone.

During the experiment we asked for explanations of the input values with which the students controlled the heat flow and the pump flow. The answers to this question never indicated an exact or quantitative représentation of the process in the mind of the student operator. An example from subject 1 (our comments are between brackets):

17th European Annual Conference on Human Décision Making and Manual Control

"Disîurbance pump-7-E

fnow its flow is 200),

I put it back to 5 (ïts

Now Tank-3-E has a low level

so I turn up Pump-8-E until 15....

Pump-6-E I put up tili 40 so the level in

Tank-4-E goes down again. "

We: "Why did you choose 40?"

"Because it is more than the flows of pump-5-E

and pump-7-E. (The upstream pumps, i.e. what flows inj.

Nothing specific Tank-4-E is back again.

Iput the pump

) back to 20. "

The subject perceives a disturbance in Pump-7-E resulting in a low-level alarm in Tank-3-E and a high level alarm in Tank-4-E (which is not acknowledged explicitly), Pump-8-E is turned up to fill Tank-3-E, Pump-7-E and Pump-5-E at that time ran at 10 so, in order to diminish the amount of liquid, the subject turns up the flow of Pump-6-E with 40 (10 +10 flows in, 40 goes out). When we asked him why he chose 40, he gave no explicit explanation. We can also see that in this case the subject did not readjust the flow to 40. After tank-4-E is no longer in alarm, the subject put pump-6-E back to 20; its steady-state value.

Another prove for the fact that the subjects did not use a quantitative input-output model is that in the data we cannot find linear relationships between observed values and inputs. E.g. If we look at a typical example of the control action of a subject (subject 3) after he has perceived an temperature-alarm (T>320 K). In Figure 3 we see the relationship between a température and the value of a control action by subject 3. We see that there is no straight line (as for example the dashed line in Figure 3) between the température and the heat-flow input. Instead we see e.g. the value of -5000 appearing 4 times for différent températures. A purely qualitative approach would have yielded the same input (Q) for every value of T. 0 100 200 300 400 500 ^1000 -5000 • « X »

1—

Figure 3. The relationship between the observed

value of T and the controlled value of Q. The

dashed line is an example ofa quantitative

input-output model. A purely qualitative approach

would be for example the horizontal line at

-5000. In that case for each alarm value the

same numeric value would have been chosen.

E X P E R I M E N T 2 W I T H

L I Q U I D - V E S S E L S Y S T E M

In another experiment we made use of a qualitative model as a concept for a human Operator support System. We used the same 4 tank System as in Figure 2. The Operator support System contained a table which showed a qualitative model of the heat transfer in the plant. There also was an expert System that reported a fault in qualitative terms.

subjects

We tested 5 subjects. Ail subjects were students of the départaient of control engineering of the Faculty of Mechanical Engineering of Delft University of Technology. (Ages between 22 and 26 years).

training

The experiment was part of a longer experiment (see [16] for détails) which included an extensive research of a HOSS for alarm handling. The qualitative experiment, we want to explain here, was at the end of about 2 hours of experimenting with the system. (This part of the experiment Iasted about twenty minutes and only concemed the qualitative model.) By the time we reached the évaluation of this qualitative interface, the subjects had a throughout understanding of the system.

Statements by the subjects —

These équations show that the température at the input (TO and the heat-flow Q influence the change in température (i.e. whether is goes up or down or stays the same). In tank-2-E, Tank-3-E and Tank-4-E, there is a heat-flow from both the left hand side and the right hand side. In tank-l-E there only is a flow from the left hand side.

Tank-l-E Tank-2-E Tank-3-E Tank-4-E

T up zero zero up

Tfrorn left" T zero zero zero zero

Tfrom ripht" T zero zero zero

Q up zero zero down

Figure

4 : A qualitative display of the heat

transfer of the system of Figure

2, represented in

a tabular •farm.

In Figure 4 we see in the first column the various variables of the (température) dynamics of the tanks (see Equation [-2-]). In the second to fourth column from the second to the fourth row, we see the qualitative status of the tanks real time. We see that the température of Tank-l-E is increasing this can be understood by the positive heat-flow (Q). In Tank-4-E something is wrong, the température seems to be increasing, although the heat flow (Q) is down (i.e. there is a heat drain). In this case, in the experiment, the expert system would send a message to the operator to tell him that an error has occurred and that probably something was wrong with one of the sensors. This message would read:

"Disturbance in tank-4-E up zero zero down"

We wanted to know if the operators understood these messages and if the interface appealed to them. Their understanding of these messages was evaluated by letting the operator explain the message.

Although the students clearly understood the messages, they did not like the shape of our représentation (the table of Figure 4). This calls for the next step in this research. If the qualitative model is indeed a common sense model and applicable for training and HOSS design, how do we represent the qualitative information?

C O N C L U S I O N S

• The subjects of experiment 1 showed a qualitative trial and error behaviour to control the faults. The subjects gave no explanation for quantitative values of input variables.

• The subjects of experiment 2 said that a qualitative représentation of heat-transfer is understandable but that représentation we choose was not appealing.

These results do not contradict our hypothesis which is that a first step in understanding a physical phenomenon an operator takes is making a qualitative représentation of that physical phenomenon. In the future we want to use this same setting to test or hypothesis for plant operators.

D I S C U S S I O N

Intelligent human operator support demands a understanding of human cognitive processes. A human operator support system that is designed without knowledge of the cognitive processes of the human operator will miss its goal. The operator can not supervise a HOSS that he does not understand. The acceptance of such a system by the operator will also be a problem. Because a HOSS that presents conclusions that are not understood by operators will lead to a shift in responsibility from the human to the machine, which is clearly not something operators accept [11]. This calls for attempts to design human operator supports Systems that take into account the knowledge of représentations of human operators.

Designing an interface based on ecological psychology [14] or that intelligently co-operates with the user [9], has been an interest of several authors. These authors try to fmd out how the human operator reasons by nature and use this knowledge for their design. We have used a verbal protocol to fmd out how a student operator reasons by nature. It is clear that plant

17th European Annual Conference on Human Décision Making and Manual Control

Operators and Student Operators are not the same. This is why we want to do a similar experiment with plant Operators in the future.

Larkin [7] says that qualitative models should be adopted to teach physics on high schools. We wonder if this qualitative model could not also be adopted for training of Operators. A simulation which only allows qualitative input of variables could be used as a training simulation. An explanation of the qualitative dependencies of this simulation could also be a part of this training.

Workers in qualitative reasoning claim to model the physical world in a human or common sense way ([1], [6]). A claim of being able to deal with human approaches of Systems in général is made by workers in fuzzy logic [15]. An important différence between the two approaches is that in fuzzy logic a quantitative value has a membership or truth of a fuzzy set which lies between 0 and 1. A quantitative value in fuzzy logic can have a membership of several fuzzy sets. (If we take for example the fuzzy sets named "not and "cold" the température 15°C has a membership of both sets.) Whereas a quantitative value in qualitative reasoning only has a membership of one of the three qualitative sets. Its membership is always 1 because a value is only a member of one set (e.g. "negative", "zéro" or "positive"). However qualitative reasoning can be seen as a special kind of fuzzy logic with three membership functions without a slope and no overlap. In Figure 5 we see three qualitative labels ("negative", "zéro" and "positive") pictured as labels of fuzzy sets. A quantitative value x has a membership of 1 of the fuzzy set "negative", "zéro" or "positive" for x<0, x=0 and x>0 respectively. — MTO -e.s- • &6

-V

-10000 -5000 0 5000 10000

Figure 5. Three qualitative values represented as

tree labels of fuzzy sets. The membership can

only take the value 0 and 1.

In Figure 6 we see an example of the same three fuzzy sets with normal membership functions.

-5000 0 5000 QUANTITATIVE V A L U E (x)

Figure 6. Three qualitative values represented as

the labels ofthe same fuzzy sets as in Figure 5.

The membership can take any value between 0

and 1 and the functions overlap.

We have hypothesised that human knowledge of a physical phenomenon evolves from the situation in Figure 5 to the situation in Figure 6.

R E F E R E N C E S

[1] Bobrow, «Qualitative reasoning about physical Systems », Elsevier Science Publishers BV, Amsterdam, 1984.

[2] K. D. Forbus, «Qualitative reasoning about space and motion », in D. Gentner and A. L . Stevens, Mental Models, Lawrence Erlbaum Ass., London, 53-73

1983.

[3] D. Gentner & A. L . Stevens, «Mental Models», Lawrence Erlbaum Ass. Publishers, London, 1983.

[4] B. Kirwan & L.K. Ainsworth, « A guide to task analysis», Taylor and Francis. London, 71-80, 1992.

[5] K. de Koning & B. Bredeweg, « A framework for teaching qualitative models», 1 Ith European Conference on Artificial Intelligence, Amsterdam, 1994.

[6] B. Kuipers, «Commonsense reasoning about causality: deriving behavior from structure », in Bobrow ed., qualitative reasoning about physical Systems,

169-203, Elsevier Science Publishers BV, Amsterdam, 1984.

[7] J.H. Larkin, « The role of problem représentation in physics », in D. Gentner and A. L . Stevens, Mental Models, Lawrence Erlbaum Ass., London, 75-98,

1983.

[8] M.-F. Legendre, « Task analysis and validation for a qualitative, exploratory curriculum, in force and motion », Instructional Science, 25(4), Kluwer Academie Publishers, 255-305 Haarlem,

1997.

[9] P. Maes, « Intelligent Software », Int. Conference on Intelligent User Interfaces, 41-43, Florida, 1997.

[10] J. Rasmussen, «Skills, rules, and knowledge; signais, signs, and symbols, and other distinctions in human performance models », EEEE Transactions on Systems, Man, and, Cybernetics, 13(3), 257-266, 1983.

[11] A.M. Sassen, «Design issues of human operator support Systems », Ph. D. Thesis, Delft university of Technology, Delft,

1993.

[12] T.B. Sheridan, « Telerobotics, automation and human supervisory control », The MIT Press, Massachusetts, 1992.

[13] H.G. Stassen, G. Johannsen, N. Moray, «Internal représentation, interna] model, human performance model, mental workload», Automática, 26(4), 811-820, Pergamon Press, Oxford, 1990.

[14] K.J. Vicente & J. Rasmussen, «The ecology of human-machine Systems II: mediating "direct perception" in complex work domains », Ecological Psychology,

2(3), 207-249, Lawrence Erlbaum Associates Inc., New Jersey, 1990.

[15] R.R. Yager, S. Ovchinnikov, R.M. Tong, H.T. Nguyen, « Fuzzy sets and applications: selected papers by L.A. Zadeh », John Wiley and Sons, New York. 1987.

[16] D. P. Wawoe « A HOSS for alarm handling», M . Sc. Thesis, A-830, Delft University of Technology, Delft, 1998.