Knowledge Based Systems (KBS) denotes a field of artificial intelligence research for the encoding of expert knowledge in computer logic as repository of “if-then” rules. Though successful instances of such systems are worthy of note (e.g. MYCIN, DENDRAL and PROSPECTOR) KBS have key limitations. Namely, an expert may establish semantic narrative to relate the rules that (s)he applies when addressing a problem, however when compiled into a machine the relationship between the rules becomes confused. When an expert applied rules, they are following an overall strategy for solving a specific class of problem, computer implementations take a more probabilistic approach to selecting which rules to ‘fire’. This leads to a second significant limitation that when choosing what rules to apply, the computer will attempt to exhaustively search the knowledge based. Experts on the other hand are able to focus the applicability of specific rules. Finally, KBS are not able to self acquire any semantic knowledge of the rule base, or even gain the experience needed to know when rules should be broken and why. KBS are expensive to develop, and require continuing maintenance to grow and evolve the way human experts naturally do.
