The Problem with Expert Systems

The dependence people place on cases poses a problem to those who treat human cognition as being primarily rule-based. Much work in artificial intelligence, for example, is done in "expert systems." These systems are based on the notion that expert knowledge consists of a collection of rules. By determining the rules an expert in a domain uses, the idea goes, we may then simulate expert behavior in that domain. Not surprisingly, expert systems have run into a significant problem: they are brittle. When faced with a problem which bends the rules, they are unable to cope. They fail because they are not grounded in cases. They are unable to fall back on the details of their experience, find a similar case, and apply it. Likewise, they are unable to use similarities between tough problems and previous experiences to update their rules. Their failure to retain cases cripples their ability to learn from their experiences.

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