TY - JOUR
T1 - Two decades of Ripple Down Rules research
AU - Richards, Debbie
PY - 2009/6
Y1 - 2009/6
N2 - Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the experts head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the systems classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.
AB - Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the experts head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the systems classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.
UR - http://www.scopus.com/inward/record.url?scp=68349148286&partnerID=8YFLogxK
U2 - 10.1017/S0269888909000241
DO - 10.1017/S0269888909000241
M3 - Article
AN - SCOPUS:68349148286
VL - 24
SP - 159
EP - 184
JO - The Knowledge Engineering Review
JF - The Knowledge Engineering Review
SN - 0269-8889
IS - 2
ER -