Expert System for Project Management

Mr. R. Sachdeva, Dr. N.R. Namburi

Siemens Ltd., New Delhi, India

1.0 Introduction

With the globalisation of business, the implementation of turnkey projects is becoming increasingly complex. Inputs from different and diverse sources, coordination and synchronizing of delivery schedules, and cost-reduction targets call for powerful, effective and intelligent tools. Without such tools, an unexpected situation can put the project manager under stress which can consequently lead to wrong decisions. Therefore, there is a necessity today for on-line systems that can diagnose the causes of problems, help the project management to arrive at quick and proper decisions as well as identify ways to take corrective actions.

The important parameters for on-line implementation of such a diagnostic system are the accuracy and speed of the diagnosis. The concepts used to realise these parameters have a strong influence on the selection of the most suitable tool for a given application. Current concepts such as Establish-Refine and Hypothesis-Test are explained in this paper. Further more, the representation of the knowledge in data files along with an appropriate illustration have also been presented here.

2 . 0 Expert System

An expert system is a machine assigned to fulfill the above demands using the synergy of

knowledge and intelligence of

comprises of the basic components as shown in fig. 1. The project data is fed into the expert system through. an appropriate project interface. The Problem - Solving Module is equipped with a pre-def ined reference model. It checks and evaluates the deviations of the actual incoming data from the reference model and accordingly flags them as harmless or suspicious. Thereafter it determines the possible cause ( s ) of the suspicious deviations.

several human beings. It

A detailed description of the diagnosis and its basis can be obtained from the Explanation Module. It is also possible for the project management to trace every step and logic of the diagnostic process.

The Expert System is provided with two dialog modules which serve as interfaces to : (a) the project management and (b) the human expert(s) for exchange of knowledge. Both these interfaces draw upon a common knowledge base provided in the machine.

3.0 Knowledge Base

The knowledge base consists of the project-specific knowledge and the basic knowledge.

The basic knowledge include information masks of a static character. For example, the names and details of the owner, consultants, clients and

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government agencies would form part of the basic knowledge. Fundamental technological characteristics and basic technical data of the main plant equipment are also included in this category. On the other hand, the project specific knowledge comprises of data and information which can be changed and updated by the project management through the 'Knowledge Acquisition Module'. Examples of such knowledge can be the prevailing market prices of raw materials or the financial health of an important sub-supplier. The dialogue with the 'Knowledge Acquisition Module' can take place on-line such that the latest valid information is available for guidance.

4.0 Problem Solving Technique

By far the most popular teshnique used by current expert systems for solving problems is the step by step approach. This method gradually reduces the set of possible solutions through plausibility checks until a final solution is found.

A sequential application of two techniques is followed. The first one is termed as 'Establish-Refine'. It is employed to reach a conclusion or a diagnosis. In case a diagnosis cannot be arrived at, a second technique 'Hypothesis and Test' is resorted to. This procedure is in a way similar to the behavior of human experts who systematically follow a line of thinking till a logical conclusion is reached. If this conclusion does not support the acceptance of the hypothesis, one reviews and- thinks of other possible causes and carries out a test on an alternative hypothesis.

4.1 Establish-Refine

This is the primary technique for problem solving. Starting from a rather general diagnosis, the diagnosis is refined to an intermediate diagnosis and finally to an end diagnosis.

This method is illustrated by Fig. 2 which shows a decision tree or network.

As soon as a symptom is detected, the diagnosis is carried a step downward which means a further refinement. If a question (symptom) cannot be answered with certainty then the system takes the path which so far was the most used one. If a diagnosis cannot be reached then the system goes back to the point where uncertainty existed and continues on the next commonly used path, as shown in Fig. 3.

In this manner all uncertain replies are handled. If a diagnosis is still not possible,the 'Establish--Xefine' is given up and 'Hypothesis and Test' starts.

4.2 Hypothesis And Test

During the forward (downward) chaining, as explained above, the different hypotheses are implicitly evaluated and provided with certainty points. If a symptom occurs that indicates the possible applicability of a certain hypothesis then this hypothesis is provided with certainty points. These points also depend on the certainty with which the existence of a symptom can be confirmed.

All hypotheses, that have more than a predefined number of points, are than included in a data base. The most probable hypothesis is checked first,

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and if necessary thereafter, the next probable one and so on.

If a final diagnosis is not reached, the expert system reports its finding and breaks off. This is the signal for human experts to intervene, use their knowledge and intelligence to solve the

subsequently update and modify the knowledge base. If a similar problem occurs after such a modification, the expert system is able to diagnose and solve problem.

problem I and then

4.3 Further Features

Data is generally read from the transfer file of the project interface and contains on-line data. This data is needed for carrying out the diagnosis and may be used many times over and is not altered till the diagnosis is completed.

An attempt is made to answer all questions internally on the basis of the collected information and no questions are put to the user. However, if questions become necessary and unavoidable, these are formulated precisely by blend- ing case-specific text with the question.

5.0 Knowledge representation

The user has to provide the system with knowledge. A typical knowledge representation in the form of a decision tree is shown in Fig. 4 . Starting from the first node, called the starting or root node, the system is provided with symptoms that have to be checked at every node. Once a basic structure is ready, text descriptions are to be provided for every diagnosis and symptom.

Extreme care has to be exercised by the user while creating this knowledge base because a defective knowledge base would come up with inaccurate or even absurd results. It must always be borne in mind that the system has no way of checking the correctness of the knowledge fed to it.

The above rules and facts are stored as PROLOG clauses and the complete knowledge base is

various into divided sub-knowledge bases. At any point of time only one (the relevant) sub-base is available in the main memory of the computer system. The loading takes place at the start of a diagnosis and its update is disabled till the diagnosis is complete.

The user also identifies, while creating the knowledge base, as to which internal signals indicate a major disturbance that calls for a diagnosis.

The knowledge base is the essence of the system. It is

specialists of the plant, process and project personnel; and therefore, very valuable to the user. The user usually prefers not to part with this knowledge and therefore likes to create this knowledge base himself. This is easily possible as modern expert systems off er a comfortable user interface which is dialog oriented and does not require the user to learn the intricacies of PROLOG or any other programming language.

the expertise of

6.0 Hardware

A typical hardware configuration is shown in fig. 5. The hardware consists of 486/RISC systems with SCO-UNIX V5 and all are interconnected

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over a high-speed Sinec H3 Ethernet.

The present work stations, equipped with very fast processors and large memory capacities, offer a high performance to price ratio. Applications are already available with X-windows to enable the user to configure and operate the expert systems with ease.

7 . 0 Conclusions

With the collapse of the Eastern Bloc, the liberalization of economies, the removal of trade barriers and tbe tremendous advancements in communications, the Earth is rapidly making a transition towards becomirig a homogeneous mass on which economic boundaries shall cease to exist. This has resulted today in fierce competition and a race for cost leadership. Traditional captive market

segments are vanishing and infernational projects with global sourcing are under execution. There. is an increasing trend towards using expert systems to handle such complex projects. Fueled by powerful processors and software, it is now possible to offer optimum, cost effective and accurate solutions encompassing all areas of project management.

In the near future we can expect a shift towards Expert Systems using decentralised work stations, with real time UNIX, and communication via satellites. This will enable complete project management starting from the lowest level to the corporate level using global networking.

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