In Early Work in AI we saw that AI's reach quickly exceeded its grasp when trying to build universal machines. One of the fundamental problems encountered became known as the general knowledge problem or the common sense knowledge problem. While researchers were aware that in an AI system, knowledge would have to be explicitly represented, they did not anticipate the vast amount of implicit knowledge we all share about the world and ourselves. Designers of AI systems did not consider producing rules like "If President Clinton is in Washington, then his left foot is also in Washington," or "If a father has a son, then the son is younger than the father and remains younger for his entire life." In retrospect, this is perhaps not surprising, because the implicit nature of this knowledge in humans means that we all take it for granted, and never have to state it or consider it explicitly.
Once the problem was acknowledged, it soon became clear that it represented an enormous hurdle for the development of general purpose intelligent systems. One hope or perhaps wishful thinking on the part of AI developers, was that all that was needed was a decent learning program, and this knowledge would be acquired by computers as automatically as it is acquired by humans. A central part of the common sense knowledge problem has to do with the issue of knowledge representation in artificial systems. What is the best approach to represent knowledge? Are dictionary- or encyclopedia-like entries the best approach? Should everything be formulated as a series of if-then rules? Should multiple forms of representation be used? It is clear that not all human knowledge is represented in such an explicit or declarative form. The implicit nature of knowledge applies not only to common sense knowledge, but also to a wide variety of expertise and skills we possess. Such domain-specific knowledge is often represented as procedures, rather than facts and rules (these difficulties will be discussed further in the section on Expert Systems). The article below discusses some of the issues relevant to knowledge representation in both humans and computers.