rules (RIMCRDR). The data structure of RDR described here is SCRDR, which is the simplest structure. The data structure is similar to a decision tree. Each node has a rule; the format of this rule is IF cond1 AND cond2 AND ... AND THEN conclusion. Cond1 is a condition (boolean evaluation), for example A=1, and . Each node has exactly two successor nodes, these successor nodes are connected to predecessor node by "ELSE" or "EXCEPT". An example of SCRDR tree (defined recursively) is shown below: IF (OutLook = "SUNNY" AND Temperature = "COOL") THEN PLAY="TENNIS" EXCEPT Child-1 ELSE Child-2 where Child-1 and Child-2 are also SCRDR trees. For example, Child-1 is: IF (Wind = "WINDY" AND Humidity = "HIGH") THEN Play="SQUASH" EXCEPT NoChild ELSE NoChild Knowledge-acquisition scenario Human experts provide a case to the system and they add a new rule to correct the classification of a misclassified case. For example, rule Child-1 is added to correct classification of case [OutLook="SUNNY", Temperature="COOL", Wind="WINDY", Humidity="HIGH", ForeCast="STORM", Play="SQUASH"]. This case is misclassified as Play="TENNIS". When a rule is constructed by the human experts, the conditions of this rule should be satisfied by the misclassified case and also they should NOT be satisfied by any previous cases classified correctly by the parent rule (which in this context is the first rule). Implementations Below is a list of known implementations of RDR: The alpha version of RDR (MCRDR) Framework was developed by the University of New South Wales (UNSW) and University of Tasmania (UTAS) Research Team and funded by ARC. RDR (MCRDR) document classifier was developed by Dr. Yang Sok Kim, UNSW and AProf. Byeong Ho Kang, UTAS. The Erudine Behaviour Engine is a commercial software product that employs RDRs as part of its methodology for knowledge capture and business process modelling. It is implemented in Java. The Ballarat Incremental Knowledge Engine (BIKE) is a comprehensive open source implementation in C++. It includes plugins for single-classification and multiple-classification RDRs. Pacific Knowledge Systems (PKS) uses a commercial product called RippleDown Expert that is based on multiple-classification ripple-down rules. The Java data-mining software Weka has a version of Induct RDR called Ridor. It learns rules from a data set with the principal aim to predict a class within a test set. RDRPOSTagger toolkit: Single-classification ripple-down rules for part-of-speech tagging RDRsegmenter toolkit: Single-classification ripple-down rules for word segmentation See also Case-based reasoning Decision trees Multiple-classification ripple-down rules