Generalizing the Order of Operators in Macro-Operators

摘要: Abstract A number of machine learning systems have been built which learn macro-operators or plan schemata , i.e. general compositions actions achieve a goal. However, previous research has not addressed the issue generalizing temporal order operators and with partially-ordered actions. This paper presents an algorithm for incorporated into EGGS domain-independent explanation-based system. Examples from domains computer programming narrative understanding are used to illustrate performance this These examples demonstrate that can result in more as well justified concepts. theoretical analysis time complexity generalization is also presented.