The work started by projects like the General Problem Solver (see Early Work in AI) and other rule-based reasoning systems (like Logic Theorist, mentioned in the same chapter) became the foundation for almost 40 years of research. Symbolic AI (or Classical AI) is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. facts and rules). If such an approach is to be successful in producing human-like intelligence then it is necessary to translate often implicit or procedural knowledge (i.e. knowledge and skills which are not readily accessible to conscious awareness) possessed by humans into an explicit form using symbols and rules for their manipulation. Symbolic AI has had some impressive successes. Artificial systems mimicking human expertise (Expert Systems, discussed later) are emerging in a variety of fields which constitute narrow but deep knowledge domains. Game playing programs are being written now that challenge the best human experts. The difficulties encountered by symbolic AI have however, been deep, possibly unresolvable ones. One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem (discussed in the next chapter). In addition, areas which rely on procedural or implicit knowledge such as sensory/motor processes, are much more difficult to handle within the Symbolic AI framework. In these fields, Symbolic AI has had limited success, and by and large has left the field to neural network architectures (discussed in a later chapter) which are more suitable for such tasks. In sections to follow we will elaborate on important sub-areas of Symbolic AI as well as difficulties encountered by this approach.