Explanation and Limits to Knowledge

Some types of knowledge-based systems may make judgments based on data that contain uncertainty we shall learn more of this in the next chapter when we encounter fuzzy systems. Even when the information that the system reasons with is unambiguous, the system s conclusions may come as a surprise to a nonexpert. If the user doubts whether the ES has reasoned correctly, it is natural for them to seek reassurance that the line of reasoning used is robust, so the ES must be able to do more than merely provide advice, it should be able to explain how it has reached a particular conclusion. 

Explanations can be triggered by a user response of Why or How We can revisit the example from the start of the chapter to see this in action  

Biologist I ve been working too hard. I need a break. 

ES If you like good food and wine, I will suggest only coun- 

Comments from the system help to give the user confidence that the ES knows what it is doing. They are particularly valuable if the ES has engaged in some complicated reasoning or if its conclusions are unexpected (though, in the example given above, the replies from the ES hardly fall in that category). 

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