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3.3. THE ROLE OF A CONCEPTUAL MODEL

Several influential models in HCI emphasize that a high-level conceptual model of the interface is of major importance to the users' ability to learn an interface and to their comfort and efficiency in using it. When users approach a new system, they are trying to build a model of what the system does and how they will get it to do it. If the interface either encourages formation of a correct model, the users' ability to learn and use a system is enhanced.

The mental model is even more important for users of expert systems. It is crucial to a user, particularly an expert in a field, that a system is doing the reasoning correctly. If users find it difficult to understand the coverage of the system, the inference processes and the match to their own reasoning processes, their ability to use the system is undermined and acceptance becomes more problematic.

To enable a user to develop the conceptual model of the processes of the expert system, it is important that the system is able to explain both the reasoning process it uses and how that process is represented in the system. Explanation is also critical to the acceptance of systems by real users. Early medical expert systems based on Bayes Theorems, for example, often arrived at a correct conclusion. However, the users were unable to predict what questions the system would ask, were unable to follow the reasoning involved, and had no access to the knowledge base of the system. These systems were not accepted by the medical community because of such difficulties. In more recent knowledge-based systems, the trend is to open up the knowledge base for users to use the knowledge for inferencing and decision-making.

The level of conceptual level is usually built on top of the semantic level. At this level the designer is concerned with the meanings conveyed by the user's input and by the computer's output. Designing the semantic level of this type of interface is a prime concern of the designer of the expert systems shell. In the case of the interface design for an expert system, this level is complicated by the number of different types of users who may be involved. The designer, for example, must consider interfaces for different tasks, such as knowledge capture and end use of the expert system.

3.4. INTELLIGENT INTERFACE

Intelligent interfaces represent the latest development in human-computer interaction and interface design. The concept of an intelligent interface was introduced to offer innovative solutions to the problems encountered in human-computer interaction. An intelligent interface can be defined as an intelligent entity mediating between two or more interacting agents who possess an incomplete understanding of each others' knowledge and form of communication. A related concept is adaptive interfaces.

3.4.1. Models of the Intelligent Interface

The concept of an intelligent interface between humans and machines requires a notion of shared intelligence and cognition that creates technical challenges for interface designers. Much of the conceptual foundation for intelligent interfaces is being laid by research on human information processing. Previous literature on intelligent interfaces has been concerned principally with delineating functional attributes. For example, it has been suggested that interfaces should enhance human-computer interaction in terms of services, style, and clarity. Intelligent interfaces should provide services such as the automation of routine tasks, and provision of easy access to tools and to online assistance and documentation. These services should be provided so as to encourage experimentation, minimize errors and be non-intrusive. Furthermore, intelligent interfaces should present a smooth transition from novice to expert modes of operation.

3.4.2. Intelligent Interface and Machine Reasoning

An intelligent interface has also been seen as a machine reasoning system. In such cases, the key components are methods for representing task knowledge, user models, and inference tools for reasoning as the task progresses. Expert systems, on the other hand, typically do not incorporate extensive user models. Expert systems employ human knowledge to perform tasks that usually require human intelligence. Expert systems appear to provide the best means for implementing machine reasoning. Intelligent interfaces that are based on expert systems technology will either need enhancements to their user model or else the use input will be constrained to be rather specific to avoid the problems of ambiguity in language.

3.4.3. Expert Systems As Intelligent Interfaces

Expert systems represent an alternative model of an intelligent interface where the interface plays a more active role in assisting the user with the performance of the task. Consider an advisory expert system where the user wants to make a decision with the assistance of an expert system. The machine reasoning capability of the intelligent interface is foremost, and the task is represented in the knowledge base and inference engine of the expert system. The discourse output machine will include the explanation facility along with the user interface tools that query the user and output the expert system's advice. The user interface will also implement the functions of the discourse input machine, structuring and parsing the user inputs into the form of facts and rules that can be recognized by the knowledge base and inference engine. Advisory expert systems represents only one type of expert system.

3.5. DIRECT MANIPULATION

Ideally, the communication between the user and the system should be so natural that the user is not even aware that he is communicating through an interface. From this perspective, direct manipulation interfaces can be seen as types of intelligent interfaces. Major issues that have been addressed in the literature of interface design include memory load, visual representation, and transition between levels of abstraction. In spite of the influence of direct manipulation concepts, almost all interfaces involve a form of dialog between the human and the computer. Furnas, Landauer, Gomez, and Dumais (1987) show that there were very low rates of agreement in the words assigned by different users to the same function.


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