Operational Expert System Research Articles

Expert systems in pavement management

This paper discusses the potential application of expert systems, a new information technology derived from artificial intelligence research, to pavement management. The principal focus concerns the development of such knowledge-based or expert systems for project-level analysis and design of pavement rehabilitation strategies. Particular emphasis is placed on the needs of locally managed highway systems, although many of the concepts are also more broadly applicable. The paper provides a brief overview of the evolution and application of expert systems. Several major issues in pavement management are discussed which strongly suggest the appropriateness of expert systems approaches. These issues include the role of computerized pavement management systems, the acquisition of pavement performance data and current procedures for pavement structural evaluation and design. Current research on PARADIGM, an integrated set of expert systems for pavement rehabilitation decision-making, is described. Finally, a PARADIGM component system consisting of an operational prototype expert system for pavement surface condition evaluation, is presented as an example.

Development of Expert System for Classifying Sedimentary Basins as Aid to Assessing Petroleum Resources: ABSTRACT

A prototype, operational expert system, called muPETROL, patterned after muPROSPECTOR, is designed to provide for a global classification of the sedimentary basins of the world as a first step in developing an integrated expert systems approach to estimating undiscovered petroleum resources. The first phase consists of nine sedimentary basin models based on the recognition criteria of H.D. Klemme's world-basin classification scheme. Each model, consisting of a rule-based system, embodies the geologic concepts of plate tectonics modified by regional tectonics and localized lithologic and depositional sequences. The system of rule based models is used to evaluate the likelihood of one or more occurrences based on the geologic characteristics deemed necessary for categorizing each basin, coupled with the geologic reasons upon which the evaluation is based. The test basin being classified is matched against known analog basins that are contained within a data base listing more than 600 world basins classified by the Klemme system. This paper focuses on (1) acquiring the geologic knowledge base (knowledge acquisition) directly from experts, published references, and data bases essential for classifying the sedimentary basins of the world, and (2) building the basin models for an expert system whose ultimate purpose is to serve as themore » first phase in an integrated program to assess the undiscovered petroleum resources remaining in the world's basins.« less

Expert Systems and Operational Research-Mutual Benefits

The aim of much operational research and expert systems work is similar-helping a decision maker do his job. Liaison will produce mutual benefits. Expert systems development will benefit from the experience of operational research. The operational research community is well placed to support and develop the use of such systems by management.

Expert System For Complex Station Management InMass Transit Railway Operation

After years of research and field trial, applications of Artificial Intelligence(AI), both in commercial and industrial sectors, have come into practice. Expert System is the most mature aspect in AI where many applications are now in operation world-wide. This paper describes the development of an AI based Station Management Expert System which is a prototype for the future comprehensive full station management system for one of the most busiest subway systems in the world.

Relational Approach To Fault Diagnosis BasedOn A Functional Model

Matra Marconi Space has been developing operational expert systems for spacecraft fault diagnosis using hybrid approaches, including techniques related to model based approach and fault trees. Keeping in mind the operational constraints of exhaustivity and efficiency that guided the initial choices, the reasoning paradigms developed are now refined, to improve the solutions reached. The paper describes some of the techniques implemented so far, and proposes extensions to the relational approach to automated diagnosis, in order to include more of the available knowledge to refine the representation of influences, by distinguishing certain and possible influences, and through a gradation of the uncertainty introduced.