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5.1. TASK CHARACTERISTICS

The nature of the ES task and the context in which the ES is used is the first design factor that will influence the amount and the types of explanations that are used. The types of tasks that an ES performs can be categorized by various classifications, including analysis tasks vs. synthesis tasks, and heuristic classification tasks vs. heuristic configuration tasks. While these classifications overlap to some extent, each of them can be further decomposed into a larger hierarchy of many levels of tasks. For example, the heuristic classification task can be decomposed into the three inference processes (subtasks) of data abstraction, heuristic match, and solution refinement. Similarly, analysis tasks can be broken down into subcategories such as diagnosis, prediction, etc. The Ye and Johnson (1995) study directly investigated the influence of varying task types on the preference for the three types of explanations, by utilizing the data abstraction and heuristic match levels of heuristic classification as independent variables. However, it did not find significant differences in the preference for explanations between the two levels. The use of explanations that are provided by ES performing synthesis or heuristic configuration tasks, such as design or planning, has not been studied. Considering the critical differences between these tasks and the more common diagnostic tasks, it is reasonable to expect that they will result in different patterns of ES explanation use.

The context in which an ES is utilized will determine the purpose for which the explanation facility is used. Three contexts for the use of ES explanations can be identified: (1) by end-users in problem-solving contexts, (2) by knowledge engineers carrying out knowledge-base debugging activities, and (3) as part of ES validation activities carried out by domain experts and/or knowledge engineers. The distinctions between these three contexts are critical and stem from the fact that the use of ES explanations in systems development is motivated by a different set of objectives than when used as part of end-user applications. It can therefore reasonably be expected that end-users of ES applications will use explanations differently from when they are used during debugging, validation, or other ES development activities.

While explanations are commonly incorporated into most end-user applications of ES, they also play a significant role in the development of ES by offering enhanced debugging and validation abilities. Most current ES development shells and environments include tools that utilize explanations to aid efficient and effective system development, e.g., the Knowledge Engineering Environment (KEE) from Intellicorp. Another example is the REPORT command in the VPExpert shell. This command lists in sequential order all the explanations attached (using the BECAUSE clause) to rules that "fired" as part of a consultation. Such a listing assists in the debugging of processing logic by knowledge engineers. It also allows users and domain experts, who may not be familiar with representation schemes and inference engines, to participate in the validation of a knowledge base.

In contrast to debugging and validation, the use of explanations by end-users of ES applications is motivated by a different set of reasons. For example, it has been suggested that an explanation facility is used: (1) by decision makers because it aids them in formulating problems and models for analysis, (2) by sophisticated users because it assures them that the system's knowledge and reasoning process is appropriate, and (3) by novice users because it can instruct them about the knowledge in the system as it is applied to solve a particular problem. There are also a variety of contexts in which end-user applications of ES are used. For example, while some applications are used as tools for training novices in a domain, others are used by experts to support their own decision-making. The organizational context in which these end-user applications are used will also affect the use of the explanations. Some organizations institutionalize the use of such ES applications for making certain critical decisions. The use of explanations when end-users are compelled to use the ES will certainly be different from the situation when end-users utilize the ES as a decision aid by choice. In summary, many different contexts of the use of ES can be identified as potentially influencing the use of ES explanations.

5.2. CHARACTERISTICS OF THE EXPLANATIONS

The nature of the explanatory information provided by an ES to its users will certainly influence the explanations that are used. These can be divided into two major categories: explanation type and explanation content. While the types of explanations were discussed earlier, there is considerable overlap between these two categories. The three types of explanations are by definition different in content. For example, the Why explanations focus on providing declarative information about the task; the How explanations provide procedural task information; and the Strategic explanations present meta-knowledge of the task. Similarly, the various types of explanations will also differ in content in relation to whether they are provided as feedforward or feedback. For example, feedback explanations, being outcome specific, will by definition be more concrete and at a lower level of specificity than the more generalized feedforward explanations.

While the influence of the types of explanations is potentially more relevant, largely because both ES developers and users distinguish clearly between them, it is also important to consider the influence of various dimensions of content. Some relevant dimensions of explanation content include the following. The informational content of the explanations in terms of the number of signals that are incorporated represents the first dimension. The second dimension is the abstraction level of the explanations, i.e., how concrete or abstract they are from the perspective of users. The third dimension is the granularity and specificity of the explanations, e.g., the lowest level will have the most amount of detail and vice versa. Fourth, explanations can be focused toward particular user groups, such as knowledge engineers, domain experts, and end-users, or they can have a more general focus. Terminological differences in explanations can be expected depending on who are the target users of the explanations. Fifth, explanations can emphasize different aspects of that which is being explained, e.g., procedural aspects in contrast to declarative aspects.


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