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Mediation of Mental Models in Process Control Through a Hypermedia Man-Machine Interface
Copyright © IFAC Man-Machine Systems, Cambridge, Massachusetts, USA, 1995
MEDIATION OF MENTAL MODELS IN PROCESS CONTROL THROUGH A HYPERMEDIA MAN-MACHINE INTERFACE
J. Heuer, S. All and M. HoUender University of Kasse~ Laboratory for Human-Machine Systems, D-34109 Kassel, Germany
Abstract: In this paper a new paradigm is de3aibed which eDhauces IXlIIveotiooal Supervis
serve as a source for corporate knowledge (Steels, 1993) that is constantly enhanced. The proposed concept fosters a continuous learning process during everyday work, which makes the learning itself highly effective (Lave & Wenger, 1991). Increasing the qualification and capabilities of the operators is one way to reduce what Bainbridge (1987) calls "the ironies of automation". Although this provides no safeguard against ''latent errors" (Reason, 1990) it does help reducing the active operator errors. Another major goal is to improve the motivation or the complete and full engagement of the person in pursuit of the end cause of the activity (Laurel, 1986), a notion for which work psyehology has coined the term task orientation.
L INIRODUCTION
In recent years there has been a tendency in process control towards increased complexity, both in the connections of components and in the overall process automation schemes. This situation highly increases the demands on the operators for effectively running a given process. Therefore the quality standards in process control do not depend solely on the characteristics of the technical process, but rely heavily on the educational standards of the operators (Sage, 1992). Additional efforts are needed to provide the knowledge and the skills that are necessary to run a complex process, especially in critical situations (Brehmer, 1987). In this paper a framework will be proposed that covers both aspects: the increased quality of the man-machine interface and the process visualisation, as well as the mediation of knowledge. The concept of mental models (Johnson-Laird, 1983) as the representational form of knowledge will be used and it is assumed that the existence of correct models is a prerequisite for successful process control (Johannsen, 1993). The process visualisation will not merely be used as a "window to the process", but will serve as a support- and learning system, which provides the operators with multiple views showing the different aspects of the system. To achieve this goal, a concept of the process visualisation system as a hypermedia-system will be proposed. This system will support the operators on different cognitive levels during supervision and control and will also
2. LEARNING OF COMPLEX SYS1EMS: A DESTII.LATION COLUMN Large technical processes represent highly complex, dynamic systems. Operators working under such conditions are expected to have stable and correct models of the interconnections between the process variables and to act accordingly. An extensive technical education as well as an intensive training with the technical process are necessary in order to develop what is called expertise (Johannsen, 1993). To model a realistic process under laboratory conditions it is necessary to use high-fidelity simulations. One such simulation was developed at the University of Stuttgart (Gilles, HolI, Marquardt, Schneider, Mahler, Brinkmann & Will, 1990). It 499
the case with causal models. e.g. models in which the interconnections between variables in a system are represented. According to Funke (1992), these models develop in three successive steps. The first steps involves the building of general hypotheses by the problem solver. These hypotheses only contain assumptions about general connections between certain variables. The second, semi-qualitative step, makes assumptions about the direction of the established connections. Finally, on the highest, quantitative level, problem solvers exactly evaluate th~ strength of these connections. The ability to build these models deteriorates if the system gets more complex and dynamic. Yet, exactly this happens in industrial and especially chemical processes and systems. For this reason it is necessary to support the operator in identifying system connections by using the adequate representation techniques.
models a chemical destillation column for the separation of benzene and toluene. A schematic flow diagram can be found in Figure 1
While the knowledge about the underlying structure of the system variables is necessary e.g. for trouble shooting and other problem solving activities, there also has to be support concerning the control-ability of the operators. As Broadbent, Fitzgerald and Broadbent (1986) have shown, there can be a considerable difference between the system knowledge somebody is able to verbalise and the quality of her control actions. This means that somebody who is able to correctly control a dynamic system does not need to have an explicit causal (mental) model and vice versa. Broadbent et al (1986) explain this by proposing two different problem solving strategies: The strategy of model manipulation assumes that the problem solver possesses a model about the system which matches the "real" situation as closely as possible. All actions and explanations for system states can be derived directly from this model. Using the strategy of si~uation matching, the problem solver has a repertorre of correct actions which are linked to certain situations. This enables the problem solver to show a good performance when confronted with most of the common system states, but she is not able to perform predictions or reasoning concerning new situation on this basis. This strategy will fail in situations not yet available in the operator's repertoire (e.g. rather seldom system failures iD chemical plants), and may result in different sorts of errors as described by Reason (1990).
Figure 1: Flow diagram of a destillatioD column
3. MENIAL MODELS AS A PREREQUISITE FOR PROCESS CONIROL The quantity and quality of mental representations an operator holds about a certain process form the basis of her successful acting even in difficult situations. These representations come in different varieties, namely as analogue representations, procedural schemata and scripts and declarative networks (Anderson, 1980). The different terms and notions of the representations will be subsumed under the term mental model. These models have both predictive and explanatory power (Norman, 1983), e.g. an operator act on a process by recognising the actual process situation and then choosing the appropriate mental model to act upon . These models (in this case schemata or scripts; Schank and Abelson, 1977) contain information that allows proper identification of the right schema for a given situation (i.e. a conditional part) and also fixed sets of actions that can be easily executed when the conditional part is matched. These schemata can be hierarchically composed of different sub-schemata, depending on the complexity of the situation. Hacker (1986) states that the correctness and level of discrimination of these models determines the quality of the operators actions, and that the quality of acting in the worlc context is mostly dependent from adequate underlying models.
Therefore the main goal of educating process control operators has to be the mediation of correct and stable mental models, on which both actions and problem solving activities can be performed. The knowledge to be taught has to contain knowledge about the technical process itself, resulting in a networlc structure or causal model. It is also necessary to provide knowledge about "how-to" control the process in many different system states.
Different studies in the area of problem solving in dynamic systems have shown (e.g. DOmer, 1986, Brehmer, 1987; Funke, 1992) that human problem solvers have great difficulties in learning such systems by active manipulation and in building an adequate mental model. This seems to be especially
500
In this paper, a MM! will be presented which incorporates the presentation of different types of mental models and thus serves to mediate koowledge on the different levels proposed. The following parts of the MM! will be presented in detail: • • •
The design model represents the conceptual model of the system to be designed. It is the task of the designer to translate these abstract models into the system image. Ideally, the system image contains different graphical representations of the original user models. The presentation of these models should provide information of as many different aspects of the technical system as possible.
a dynamic causal model a qualitative simulation enabling the operator to explore the process in different situations a dynamic blackboard- and help system.
5. USING HYPERMEDIA FOR MAN-MAClllNE IN1ERFACES IN PROCESS CONlROL Many processes have hundreds or even thousands of variables. S&C systems must group information about these variables into screens and relate the screens with each other. This is an amazing analogy to the nodes and links of the bypermedia world. Indeed, modern S&C systems can be more than just a mimicry of the former switchboards. Today, screens can be enriched with knowledge. They can be used as a means for communication between different shifts or between experienced and junior operators. A well defined structure offers "predictable paths to expected information" (Herrstrom and Massey, 1989). This structure should be essentially hierarchic but can be enhanced with other task-oriented relations like causal relations (Hollender, 1994).
4. DIFFERENT MODELS OF TIIE PROCFSS The contents which are represented in the MM! are essentially based on different Mental Models of the user concerning aspects of the process. The information which forms the basis for the representations was gathered by studies of written material, questioning experienced operators and an extensive task analysis. This gathering of content to be presented forms one part of a circular process, the goal of whicb consists in a tight match of the design model, the User Model and the System Image (Norman, 1983). Figure 2 gives an overview of the process and the different models involved.
6. HYPERMEDIA - BASED INIERFACES AS LEARNING ENVIRONMENTS The use of hypermedia-based man-machine interfaces serves mainly one purpose: To provide the operator with different views of the same process. The reason to do so comes from the special abilities and weaknesses of human information processing. Anderson (1983) applies different mechanisms on the choice of a specific production rule. One of the most important of these mechanisms is the degree of match between the given situation and the conditional term of a production rule. The more specific and exact this match is, the more likely is a production to fire. Under the assumption that the choice of situation-specific scbemata for controlling actions is based on similar mechanisms it can be said that specific schemata become activated when their "slots" match the data in the given situation. If no schemata exist that have a "perfect" match, decision procedures are involved that may possibly lead to the choice of the wrong schema (e.g. "frequency gambling" or "similarity matching", Reason, 1990). The more differentiated and specific the existing schemata are, the more precise the matching procedures can be. The bypermedia-based MM! provides the mechanisms to tune the existing schemata into a more specific form. Also, the different views of the system can be used to provide more and different information for the matching
Figure 2: Different Models of a process control system (after Norman, 1986) The data stemming from the analysis is necessarily incomplete and does not allow for an exact match of the individual user model. Therefore it is important to choose the level of resolution (Shen and Leitch, 1992) for building the abstract "typical" user model (Norman, 1983). Level of resolution in this case refers to the absolute number of existing variables in the tecbnical system (about 120 in the destillation column) and the number of salient variables (the cognitively most prominent variables). The model that was identified in this way contained merely the six input and six state variables that account for most of the dynamics in the process.
501
procedure in the case of uncertainty (i.e. no conditional parts are exactly matching).
model was developed in addition to the topological presentation.
If, for example, an operator encounters a situation where the system for some reason gets unstable, the first step would be to recognize the cause for the instability. This would be the fault hypotheses of the operator. Using the standard (topological) display, the only data for accepting or rejecting the operators fault hypothesis comes from two sources: 1be display and the operators knowledge. Since these sources are not independent, the operator will selectively search the display for confmning evidence. This results in a fair probability of erroneously interpreting the only existing data source (the display). On the other hand, in using the multimodal (multimedia) displays, the operator may gather his data on a causal, a procedural and a goal oriented basis. In addition to this (always under the assumption that there is enough time available) she might also request data from the blackboard- and help system. This cooperation of different perspectives differentiates the data the operator is using for confmning her hypotheses, thus leading to a much frrmer basis for the following course of action to take (i.e. accepting a given hypotheses as the conditional part of a production or schema).
This model consists of different nodes, each of which is representing a specific system variable. The nodes are distinguished in two different types. 1be first type - direct nodes - represents a process variable that can be directly manipulated by the user. 1be second type - indirect nodes - stands for variables that can only be influenced via the direct ones. The links between the different nodes have a double functionality. On the one hand they represent the strength of influence a node is exerting on another node. On the other hand, links are used to represent the time it takes for a manipulation on one node to influence the connected node. The visualisation of the network enables the operators to set the visual focus to the node (variable) they are currently interested in. This node is presented in the centre of the screen. The nodes exerting influence on the central node are presented to the left, ordered according to the strength of their influence and the response time. Variables being influenced by the central node are shown on the right, ordered accordingly. The operators can select every preceding or succeeding node, thus being able to follow complete cause-effect chains. The visualisation of the causal model is intended to support the supervisory planning step. Figure 3 presents an ovezview of the qualitative causal model.
6.1. The Causal Model and Simulation The "backbone" of the proposed hypermedianetwork is a causal model of the system. Although the often used topological representation of a process, providing a schematic "map" of a system, proves to be very useful in directing fteld operators and providing a "mental map" for the operators, this presentation is not sufficient. One of the major drawbacks of the topological representation is the fact that variables (or components of the system) that functionally have 00 or very little influence on each other may be shown closely grouped together; on the other hand there may be very tightly coupled variables which are presented far apart from each other. To overcome these difficulties, the causal
6.2. The Hypermedia Help- and Blackboard System In addition to the multimodal presentation the operators will be supported by a dynamic blackboard- and help system. The information contained in this system may contain textual, pictorial and dynamic elements. Every operator can add new elements to the blackboard which are immediately available to all other personnel. Information contained · in this blackboard will be evaluated in regular sessions and will be eventually become integrated in the help-system network as new nodes and links.
502
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AB
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BotbnRllingL...eo.iel Bcn:ms (Prodd) Botbn TerrT1l9rat1xe Feed Feed Tempe!a1i.J'e HeadProdd Head TefTl)9rature McHe T8fTl)9rat1xe Pressure Heating (Aeboiler)
AefkJx Catainer AefkJx (Ratio)
Figure 3: Qualitative causal model of the destillation column
+
connections. Every qualitative object contains different aUributes such as a fuzzy variable, fuzzy sets, a local fuzzy rule base about change-of-rates, a delay time etc. The values off aUributes are suitable to animate the causal-oriented user displays.
7. IMPLEMENTATION CONCEPT OF THE INFO SYSTEM The INFO-MS (Information, Navigation, Failure handling and Organisation - Management System) is implemented in a shell structure. The basic elements of the kernel are module- and process variable objects. The element organisation is managed with the help of dynamically linked lists. The initial data for the dynamic lists can be loaded from an ASCn me (process model me) or from an object-oriented database. The process model file contains the structure of the plant in a hierarchical form.
Figure 4 shows a minimised causal model Oower left), the expanded links display (upper right) and the corresponding topological view (lower right) is An example of the INFO - MMI.
8. CONCLUSION In this paper it was shown how hypermedia-based man-machine interfaces can be used to create a manifold, yet consistent representation of a complex technical process. Through the use of multimodal presentation techniques it is possible to mediate mental models concerning different areas of knowledge, namely procedural and declarative mental models. The mediation of these different models provides the process control operators with a solid based for their supervisory and control tasks
The INFO MM! visualises the process in several representations, such as topology, causal nets or goal oriented models, and gets the animation data from the INFO-MS. The INFO-MS gets the actualisation data either from the technical process, animation objects (simple simulations) or the hypermedia system HypMed. The animation data of HypMed were recorded previously from critical process situations and managed from a play-back driver. Thus, there is a simulation database of critical process situations that can be used any time for training purposes. The structure of the database of the hypermedia system HypMed is based on the qualitative causal model, process states and goal oriented hierarchies. The qualitative model contains qualitative objects such as process variables and
(Sherldan, 1993). Currently, extensive experiments are carried out to determine the amount and types of knowledge mediated by the interface. This is done by using different learning environments. The effect of these different representations will be assessed with help of several relevant scenarios. The experiences
503
Jobannsen, G. (1993) Mensch-Maschine-Systeme . Berlin: Springer. Johnsoo-Laird. (1983). Mental Models. Cambridge, MA: Harvard University Press. Laurel, B. K. (1986). Interfaces as Mimesis. In: D. A. Norman and S. W. Draper: User Centered System Design. Hillsdale, NJ.: Lawrence Erlbaum. Lave, J., Wenger, E.(1991). Situated Learning: Legitimate Peripheral Participation. Cambridge; MA: University Press. Perrow, C. (1984). Normal Accidents: living with high-risk technologies. New York: Basic Books Reason, J. (1990). Human Error. New York: Cambridge University Press. Sage, A. P. (1992). Systems Engineering. New York: Wiley. Scbank, R.c. and Abelson, R.P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Earlbaum Associates. Sheridan, T. (1993). Telerobotics, Automation and Supervisory Control. Cambridge, MA: MIT Press. Steels, L. (1993). Corporate Knowledge Management. AI-Memo, Vrije Universiteit Brussel.
already made with the new representation techniques give hope to a successful application of the complete system in the future.
REFERENCES Bainbridge, L. (1987).The ironies of automation. In: 1. Rasmussen, K. Duncan and J. Leplat New Technology and Human Error, 111-120.
Chichester: Wiley. Brehmer, B. (1987). Development of mental models for decision in technological systems. In: 1. Rasmussen, K. Duncan and J. Leplat New Technology and Human Error, 111 -120.
Chichester: Wiley. Gilles. E.D., Holl, P., Marquardt, W., Schneider, H., Mahler, R. , Brinkmann, K. & Will, K.-H. (1990). Ein Trainingssimulator zur Ausbildung von Betriebspersonal in der Chemischen Industrie. Automatisierungstechnische Praxis, 7, 344-350. Herrstrom, D. S., Massey, D. G. (1989) Hypertext in context. In Barren. E., The Society of Text. MIT Press. Cambridge. pp. 45-58. Hollender, M. (1994). Diagnosing faults cooperatively and in context. Preprints SAFEPROCESS'94. Vol. IT, pp. 643-648
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MEDIATION OF MENTAL MODELS IN PROCESS CONTROL THROUGH A HYPERMEDIA MAN-MACHINE INTERFACE
J. Heuer, S. All and M. HoUender University of Kasse~ Laboratory for Human-Machine Systems, D-34109 Kassel, Germany
Abstract: In this paper a new paradigm is de3aibed which eDhauces IXlIIveotiooal Supervis
serve as a source for corporate knowledge (Steels, 1993) that is constantly enhanced. The proposed concept fosters a continuous learning process during everyday work, which makes the learning itself highly effective (Lave & Wenger, 1991). Increasing the qualification and capabilities of the operators is one way to reduce what Bainbridge (1987) calls "the ironies of automation". Although this provides no safeguard against ''latent errors" (Reason, 1990) it does help reducing the active operator errors. Another major goal is to improve the motivation or the complete and full engagement of the person in pursuit of the end cause of the activity (Laurel, 1986), a notion for which work psyehology has coined the term task orientation.
L INIRODUCTION
In recent years there has been a tendency in process control towards increased complexity, both in the connections of components and in the overall process automation schemes. This situation highly increases the demands on the operators for effectively running a given process. Therefore the quality standards in process control do not depend solely on the characteristics of the technical process, but rely heavily on the educational standards of the operators (Sage, 1992). Additional efforts are needed to provide the knowledge and the skills that are necessary to run a complex process, especially in critical situations (Brehmer, 1987). In this paper a framework will be proposed that covers both aspects: the increased quality of the man-machine interface and the process visualisation, as well as the mediation of knowledge. The concept of mental models (Johnson-Laird, 1983) as the representational form of knowledge will be used and it is assumed that the existence of correct models is a prerequisite for successful process control (Johannsen, 1993). The process visualisation will not merely be used as a "window to the process", but will serve as a support- and learning system, which provides the operators with multiple views showing the different aspects of the system. To achieve this goal, a concept of the process visualisation system as a hypermedia-system will be proposed. This system will support the operators on different cognitive levels during supervision and control and will also
2. LEARNING OF COMPLEX SYS1EMS: A DESTII.LATION COLUMN Large technical processes represent highly complex, dynamic systems. Operators working under such conditions are expected to have stable and correct models of the interconnections between the process variables and to act accordingly. An extensive technical education as well as an intensive training with the technical process are necessary in order to develop what is called expertise (Johannsen, 1993). To model a realistic process under laboratory conditions it is necessary to use high-fidelity simulations. One such simulation was developed at the University of Stuttgart (Gilles, HolI, Marquardt, Schneider, Mahler, Brinkmann & Will, 1990). It 499
the case with causal models. e.g. models in which the interconnections between variables in a system are represented. According to Funke (1992), these models develop in three successive steps. The first steps involves the building of general hypotheses by the problem solver. These hypotheses only contain assumptions about general connections between certain variables. The second, semi-qualitative step, makes assumptions about the direction of the established connections. Finally, on the highest, quantitative level, problem solvers exactly evaluate th~ strength of these connections. The ability to build these models deteriorates if the system gets more complex and dynamic. Yet, exactly this happens in industrial and especially chemical processes and systems. For this reason it is necessary to support the operator in identifying system connections by using the adequate representation techniques.
models a chemical destillation column for the separation of benzene and toluene. A schematic flow diagram can be found in Figure 1
While the knowledge about the underlying structure of the system variables is necessary e.g. for trouble shooting and other problem solving activities, there also has to be support concerning the control-ability of the operators. As Broadbent, Fitzgerald and Broadbent (1986) have shown, there can be a considerable difference between the system knowledge somebody is able to verbalise and the quality of her control actions. This means that somebody who is able to correctly control a dynamic system does not need to have an explicit causal (mental) model and vice versa. Broadbent et al (1986) explain this by proposing two different problem solving strategies: The strategy of model manipulation assumes that the problem solver possesses a model about the system which matches the "real" situation as closely as possible. All actions and explanations for system states can be derived directly from this model. Using the strategy of si~uation matching, the problem solver has a repertorre of correct actions which are linked to certain situations. This enables the problem solver to show a good performance when confronted with most of the common system states, but she is not able to perform predictions or reasoning concerning new situation on this basis. This strategy will fail in situations not yet available in the operator's repertoire (e.g. rather seldom system failures iD chemical plants), and may result in different sorts of errors as described by Reason (1990).
Figure 1: Flow diagram of a destillatioD column
3. MENIAL MODELS AS A PREREQUISITE FOR PROCESS CONIROL The quantity and quality of mental representations an operator holds about a certain process form the basis of her successful acting even in difficult situations. These representations come in different varieties, namely as analogue representations, procedural schemata and scripts and declarative networks (Anderson, 1980). The different terms and notions of the representations will be subsumed under the term mental model. These models have both predictive and explanatory power (Norman, 1983), e.g. an operator act on a process by recognising the actual process situation and then choosing the appropriate mental model to act upon . These models (in this case schemata or scripts; Schank and Abelson, 1977) contain information that allows proper identification of the right schema for a given situation (i.e. a conditional part) and also fixed sets of actions that can be easily executed when the conditional part is matched. These schemata can be hierarchically composed of different sub-schemata, depending on the complexity of the situation. Hacker (1986) states that the correctness and level of discrimination of these models determines the quality of the operators actions, and that the quality of acting in the worlc context is mostly dependent from adequate underlying models.
Therefore the main goal of educating process control operators has to be the mediation of correct and stable mental models, on which both actions and problem solving activities can be performed. The knowledge to be taught has to contain knowledge about the technical process itself, resulting in a networlc structure or causal model. It is also necessary to provide knowledge about "how-to" control the process in many different system states.
Different studies in the area of problem solving in dynamic systems have shown (e.g. DOmer, 1986, Brehmer, 1987; Funke, 1992) that human problem solvers have great difficulties in learning such systems by active manipulation and in building an adequate mental model. This seems to be especially
500
In this paper, a MM! will be presented which incorporates the presentation of different types of mental models and thus serves to mediate koowledge on the different levels proposed. The following parts of the MM! will be presented in detail: • • •
The design model represents the conceptual model of the system to be designed. It is the task of the designer to translate these abstract models into the system image. Ideally, the system image contains different graphical representations of the original user models. The presentation of these models should provide information of as many different aspects of the technical system as possible.
a dynamic causal model a qualitative simulation enabling the operator to explore the process in different situations a dynamic blackboard- and help system.
5. USING HYPERMEDIA FOR MAN-MAClllNE IN1ERFACES IN PROCESS CONlROL Many processes have hundreds or even thousands of variables. S&C systems must group information about these variables into screens and relate the screens with each other. This is an amazing analogy to the nodes and links of the bypermedia world. Indeed, modern S&C systems can be more than just a mimicry of the former switchboards. Today, screens can be enriched with knowledge. They can be used as a means for communication between different shifts or between experienced and junior operators. A well defined structure offers "predictable paths to expected information" (Herrstrom and Massey, 1989). This structure should be essentially hierarchic but can be enhanced with other task-oriented relations like causal relations (Hollender, 1994).
4. DIFFERENT MODELS OF TIIE PROCFSS The contents which are represented in the MM! are essentially based on different Mental Models of the user concerning aspects of the process. The information which forms the basis for the representations was gathered by studies of written material, questioning experienced operators and an extensive task analysis. This gathering of content to be presented forms one part of a circular process, the goal of whicb consists in a tight match of the design model, the User Model and the System Image (Norman, 1983). Figure 2 gives an overview of the process and the different models involved.
6. HYPERMEDIA - BASED INIERFACES AS LEARNING ENVIRONMENTS The use of hypermedia-based man-machine interfaces serves mainly one purpose: To provide the operator with different views of the same process. The reason to do so comes from the special abilities and weaknesses of human information processing. Anderson (1983) applies different mechanisms on the choice of a specific production rule. One of the most important of these mechanisms is the degree of match between the given situation and the conditional term of a production rule. The more specific and exact this match is, the more likely is a production to fire. Under the assumption that the choice of situation-specific scbemata for controlling actions is based on similar mechanisms it can be said that specific schemata become activated when their "slots" match the data in the given situation. If no schemata exist that have a "perfect" match, decision procedures are involved that may possibly lead to the choice of the wrong schema (e.g. "frequency gambling" or "similarity matching", Reason, 1990). The more differentiated and specific the existing schemata are, the more precise the matching procedures can be. The bypermedia-based MM! provides the mechanisms to tune the existing schemata into a more specific form. Also, the different views of the system can be used to provide more and different information for the matching
Figure 2: Different Models of a process control system (after Norman, 1986) The data stemming from the analysis is necessarily incomplete and does not allow for an exact match of the individual user model. Therefore it is important to choose the level of resolution (Shen and Leitch, 1992) for building the abstract "typical" user model (Norman, 1983). Level of resolution in this case refers to the absolute number of existing variables in the tecbnical system (about 120 in the destillation column) and the number of salient variables (the cognitively most prominent variables). The model that was identified in this way contained merely the six input and six state variables that account for most of the dynamics in the process.
501
procedure in the case of uncertainty (i.e. no conditional parts are exactly matching).
model was developed in addition to the topological presentation.
If, for example, an operator encounters a situation where the system for some reason gets unstable, the first step would be to recognize the cause for the instability. This would be the fault hypotheses of the operator. Using the standard (topological) display, the only data for accepting or rejecting the operators fault hypothesis comes from two sources: 1be display and the operators knowledge. Since these sources are not independent, the operator will selectively search the display for confmning evidence. This results in a fair probability of erroneously interpreting the only existing data source (the display). On the other hand, in using the multimodal (multimedia) displays, the operator may gather his data on a causal, a procedural and a goal oriented basis. In addition to this (always under the assumption that there is enough time available) she might also request data from the blackboard- and help system. This cooperation of different perspectives differentiates the data the operator is using for confmning her hypotheses, thus leading to a much frrmer basis for the following course of action to take (i.e. accepting a given hypotheses as the conditional part of a production or schema).
This model consists of different nodes, each of which is representing a specific system variable. The nodes are distinguished in two different types. 1be first type - direct nodes - represents a process variable that can be directly manipulated by the user. 1be second type - indirect nodes - stands for variables that can only be influenced via the direct ones. The links between the different nodes have a double functionality. On the one hand they represent the strength of influence a node is exerting on another node. On the other hand, links are used to represent the time it takes for a manipulation on one node to influence the connected node. The visualisation of the network enables the operators to set the visual focus to the node (variable) they are currently interested in. This node is presented in the centre of the screen. The nodes exerting influence on the central node are presented to the left, ordered according to the strength of their influence and the response time. Variables being influenced by the central node are shown on the right, ordered accordingly. The operators can select every preceding or succeeding node, thus being able to follow complete cause-effect chains. The visualisation of the causal model is intended to support the supervisory planning step. Figure 3 presents an ovezview of the qualitative causal model.
6.1. The Causal Model and Simulation The "backbone" of the proposed hypermedianetwork is a causal model of the system. Although the often used topological representation of a process, providing a schematic "map" of a system, proves to be very useful in directing fteld operators and providing a "mental map" for the operators, this presentation is not sufficient. One of the major drawbacks of the topological representation is the fact that variables (or components of the system) that functionally have 00 or very little influence on each other may be shown closely grouped together; on the other hand there may be very tightly coupled variables which are presented far apart from each other. To overcome these difficulties, the causal
6.2. The Hypermedia Help- and Blackboard System In addition to the multimodal presentation the operators will be supported by a dynamic blackboard- and help system. The information contained in this system may contain textual, pictorial and dynamic elements. Every operator can add new elements to the blackboard which are immediately available to all other personnel. Information contained · in this blackboard will be evaluated in regular sessions and will be eventually become integrated in the help-system network as new nodes and links.
502
8L BP Br
FD FT
I-F
HT MT PO
AB
~
RF
BotbnRllingL...eo.iel Bcn:ms (Prodd) Botbn TerrT1l9rat1xe Feed Feed Tempe!a1i.J'e HeadProdd Head TefTl)9rature McHe T8fTl)9rat1xe Pressure Heating (Aeboiler)
AefkJx Catainer AefkJx (Ratio)
Figure 3: Qualitative causal model of the destillation column
+
connections. Every qualitative object contains different aUributes such as a fuzzy variable, fuzzy sets, a local fuzzy rule base about change-of-rates, a delay time etc. The values off aUributes are suitable to animate the causal-oriented user displays.
7. IMPLEMENTATION CONCEPT OF THE INFO SYSTEM The INFO-MS (Information, Navigation, Failure handling and Organisation - Management System) is implemented in a shell structure. The basic elements of the kernel are module- and process variable objects. The element organisation is managed with the help of dynamically linked lists. The initial data for the dynamic lists can be loaded from an ASCn me (process model me) or from an object-oriented database. The process model file contains the structure of the plant in a hierarchical form.
Figure 4 shows a minimised causal model Oower left), the expanded links display (upper right) and the corresponding topological view (lower right) is An example of the INFO - MMI.
8. CONCLUSION In this paper it was shown how hypermedia-based man-machine interfaces can be used to create a manifold, yet consistent representation of a complex technical process. Through the use of multimodal presentation techniques it is possible to mediate mental models concerning different areas of knowledge, namely procedural and declarative mental models. The mediation of these different models provides the process control operators with a solid based for their supervisory and control tasks
The INFO MM! visualises the process in several representations, such as topology, causal nets or goal oriented models, and gets the animation data from the INFO-MS. The INFO-MS gets the actualisation data either from the technical process, animation objects (simple simulations) or the hypermedia system HypMed. The animation data of HypMed were recorded previously from critical process situations and managed from a play-back driver. Thus, there is a simulation database of critical process situations that can be used any time for training purposes. The structure of the database of the hypermedia system HypMed is based on the qualitative causal model, process states and goal oriented hierarchies. The qualitative model contains qualitative objects such as process variables and
(Sherldan, 1993). Currently, extensive experiments are carried out to determine the amount and types of knowledge mediated by the interface. This is done by using different learning environments. The effect of these different representations will be assessed with help of several relevant scenarios. The experiences
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Jobannsen, G. (1993) Mensch-Maschine-Systeme . Berlin: Springer. Johnsoo-Laird. (1983). Mental Models. Cambridge, MA: Harvard University Press. Laurel, B. K. (1986). Interfaces as Mimesis. In: D. A. Norman and S. W. Draper: User Centered System Design. Hillsdale, NJ.: Lawrence Erlbaum. Lave, J., Wenger, E.(1991). Situated Learning: Legitimate Peripheral Participation. Cambridge; MA: University Press. Perrow, C. (1984). Normal Accidents: living with high-risk technologies. New York: Basic Books Reason, J. (1990). Human Error. New York: Cambridge University Press. Sage, A. P. (1992). Systems Engineering. New York: Wiley. Scbank, R.c. and Abelson, R.P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Earlbaum Associates. Sheridan, T. (1993). Telerobotics, Automation and Supervisory Control. Cambridge, MA: MIT Press. Steels, L. (1993). Corporate Knowledge Management. AI-Memo, Vrije Universiteit Brussel.
already made with the new representation techniques give hope to a successful application of the complete system in the future.
REFERENCES Bainbridge, L. (1987).The ironies of automation. In: 1. Rasmussen, K. Duncan and J. Leplat New Technology and Human Error, 111-120.
Chichester: Wiley. Brehmer, B. (1987). Development of mental models for decision in technological systems. In: 1. Rasmussen, K. Duncan and J. Leplat New Technology and Human Error, 111 -120.
Chichester: Wiley. Gilles. E.D., Holl, P., Marquardt, W., Schneider, H., Mahler, R. , Brinkmann, K. & Will, K.-H. (1990). Ein Trainingssimulator zur Ausbildung von Betriebspersonal in der Chemischen Industrie. Automatisierungstechnische Praxis, 7, 344-350. Herrstrom, D. S., Massey, D. G. (1989) Hypertext in context. In Barren. E., The Society of Text. MIT Press. Cambridge. pp. 45-58. Hollender, M. (1994). Diagnosing faults cooperatively and in context. Preprints SAFEPROCESS'94. Vol. IT, pp. 643-648
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