Problems with traditional KBs: rule-based expert systems(Watson, Leake, Bachant,.) Knowledge acquisition it is difficult to obtain generalized knowledge from SM processes due to the lack of basic understanding and unstructured nature problem domain. When problem domain is not well defined, the rules formulated are imperfect and produce unreliable solutions Knowledge elicitation it is difficult and laborious to extract empirical knowledge from human experts and formalize the knowledge into decision rules that can characterize the expert performance. However many rule-based systems assumed that expert knowledge is available and can be elicited and organized efficientlyProblems with traditional KBS: rule-based expert systems (Watson, Leake, Bachant, …) • Knowledge acquisition__ it is difficult to obtain generalized knowledge from SM processes due to the lack of basic understanding and unstructured nature of problem domain. When problem domain is not well defined, the rules formulated are imperfect and produce unreliable solutions • Knowledge elicitation__ it is difficult and laborious to extract empirical knowledge from human experts and formalize the knowledge into decision rules that can characterize the expert performance. However many rule-based systems assumed that expert knowledge is available and can be elicited and organized efficiently