Abstract: A system for estimating the effort necessary to complete a project comprises a rule-based expert system including groups of related estimation rules (rule groups) and a question table, a neural network, the neurodes of which represent groups of the detailed tasks (task groups), a rule indirection module for coupling the outputs of the rule-based expert system to selected neurodes, and an inference engine for implementing the rules of the expert system. The expert system, neural network, and rule indirection module are organized as an advisor module, which communicates information between an advisor interface and the question table. Each rule group represents a quantifiable feature of the specific client/server implementation, such as its size or fault tolerance requirements. The inference engine applies the rule groups to user provided data, and each rule group generates an effort factor (EF) that provides a measure of the associated feature's potential impact on the task groups.

Abstract: A quality driven expert system maps answers input by a user in a user questionnaire into a set of qualities descriptive of a desired output entity. A library of output entities, such as network architectures is provided, each described by values for various ones of the qualities. The qualities derived by the expert system from the user's answers are matched against the qualities of the various output entities in the library to determine which entities match the specified qualities. A score for each output entity is determined by an associative matching algorithm as a function of the degree of match between each entity and the specified qualities, along with an explanation of the degree of match between each specified quality and the qualities of the output entity. A selected number of the highest scoring entities are returned to the user to further inspection and comparison.