knowledge management in construction
2.1 Expert System methodologies The success of any Expert System (ES) relies mainly on the ability to formalize and represent the knowledge within a discipline. Often this knowledge consists in a collection of subjective, incomplete, ill-defined, and informal information. Indeed, a side benefit of expert system development is the formal organization of information that was previously unexpressed. ESs are codified as a branch of applied Artificial Intelligence (AI) and their basic concept is to simulate the action of an expert to solve a specific problem by using computer aided technologies. As before mentioned, expert systems are used to address a number of problem-solving activities. In many cases, it has been demonstrated that if a tool is fine to solve a problem related to an activity (e.g., design), the same tool is inadequate to solve a problem for a different activity (e.g., planning). To address this situation, the AI research branch, including researchers and software developers and vendors, turned their attention to developing the so called “domain-specific tools�. A domain-specific tool is defined as a tool designed to be used to develop an expert system for a particular problem-solving activity. In the table below the common typologies of problems addressed by expert system developers are listed, while their detailed description is later reported in order to provide the reader of this monography with a complete overview. The proposed list is adapted from (Hayes-Roth et al., 1983).
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Table 1 List of problems solved by Expert Systems
Problem to solve
Problem description
Design Simulation Planning
Configuring objects with constraints Modeling system components and their interaction Selecting and sequencing activities according to a set of constraints to achieve a predefined goal Assigning times, costs and resources to the set of activities in a plan Selecting the best choice from a number of possibilities which respond to a number of predefined objectives Identifying solution to solve system malfunctions Inferring likely consequences of given situations Diagnosing, debugging, and repairing student behaviour Inferring system malfunctions from observables Comparing observations to expectations Governing system behaviour to meet specifications
Scheduling Selection Prediction Interpretation Instruction Diagnosis Monitoring Control
