GEOGRAPHICAL INFORMATION SYSTEMS AND EXPERT SYSTEMS FOR IMPACT ASSESSMENT Part II: Expert Systems and Decision Support Systems

2000 ◽  
Vol 02 (03) ◽  
pp. 415-448 ◽  
Author(s):  
AGUSTIN RODRIGUEZ-BACHILLER
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Expert Systems ◽  
Impact Assessment ◽  
Decision Support Systems ◽  
Geographical Information Systems ◽  
Support Systems ◽  
Geographical Information ◽  
Environmental Prediction ◽  
User Friendly

This paper and another one preceding it investigate the potential of computer technologies like Geographical Information Systems (GIS) and Expert Systems (ES) to help with Impact Assessment (IA), suggesting that one way to optimise the effectiveness of GIS is to embed these systems in a more user-friendly environment. This paper explores the experience and potential of ES to provide such decision support environment, extending the argument further into the realm of Decision Support Systems (DSS). The proposition behind this paper is that these areas (IA, GIS, ES and maybe DSS) are potentially complementary and there can be mutual benefits if they are brought together. Certain tasks in IA — like screening, scoping, or statement review — already have potential for substantial automation, as an opportunity for technology transfer within organisations involved with IA, so that relatively scarce experts can be freed up for more difficult jobs while less expert staff are promoted to answer more sophisticated questions like these. More technical tasks like impact and environmental prediction also show scope for some degree of modelling-based automation, while other less routinised and more open-ended tasks involved with the management of the whole IA process probably lend themselves better to automation of a different kind, which works more as an "aid" than a substitute for the expert, for which the more flexible DSS seem more appropriate.

GEOGRAPHICAL INFORMATION SYSTEMS AND EXPERT SYSTEMS FOR IMPACT ASSESSMENT Part I: GIS

2000 ◽  
Vol 02 (03) ◽  
pp. 369-414 ◽  
Author(s):  
AGUSTIN RODRIGUEZ-BACHILLER
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Expert Systems ◽  
Impact Assessment ◽  
Geographical Information Systems ◽  
Best Practice ◽  
Spatial Information ◽  
Geographical Information ◽  
Decision Support Tools ◽  
Support Tools

Impact Assessment (AI) is getting over its "teething problems" of the early 1990s, and is now trying to define, consolidate and spread best practice. It has been suggested that computer technologies like Geographical Information Systems (GIS) and/or decision-support tools like Expert Systems (ES) can play an important part in this process, and in a series of two articles we critically review these technologies, their use and their potential — and drawbacks — for IA. This first article is devoted to GIS, powerful packages that can handle spatial information quite efficiently in map form, but whose analytical capabilities are rather limited and would benefit from being articulated with other tools to make their use more friendly and efficient. The proposition underlying these articles is that these three areas (IA, GIS and ES), if properly organised, are potentially complementary and there can be mutual benefits if they are brought together. We concentrate on GIS first, ES will be discussed in the next paper.

scholarly journals DECISION SUPPORT SYSTEM FOR SOLVING THE STREET ROUTING PROBLEM

Transport ◽  
2008 ◽  
Vol 23 (3) ◽  
pp. 230-235 ◽  
Author(s):  
Peter Matis
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Geographical Information Systems ◽  
Support Systems ◽  
Optimal Solution ◽  
Real Data ◽  
Geographical Information ◽  
Routing Problem ◽  
Time Required ◽  
Number Of Customers

Servicing a large number of customers in a city zone is often a considerable part of many logistics chains. The capacity of one delivery vehicle is limited, but, at the same time, it usually serves plenty of customers. This problem is often called a Street Routing Problem (SRP). Key differences between Vehicle Routing Problem (VRP) and SRP are presented here. The main problem of SRP is that when the number of customers is huge, the number of delivery path combinations becomes enormous. As the experimental results show in the case of SRP the error on the length of delivery routes based on an expert's judgment when compared to the optimal solution is in the range of 10–25%. As presented in the paper, only using decision support systems such as Geographical Information Systems (GIS) makes possible to effectively manage SRP. Besides classical measurements used in VRP, such as total length of routes or time required for delivery in each route, other measurements, mostly qualitative ones, are presented. All of these are named as visual attractiveness. This paper discusses possible relationships between quantitative and qualitative measurements that give a promise for finding better solutions of SRP. Several new types of heuristics for solving SRP are evaluated and afterward compared using the real data. One of the key properties of GIS to use routing software is its flexible interactive and user‐friendly environment. Routing software can find a good solution and explore the possibilities while an expert later can change the calculated routes to explore other possibilities based on the expert's judgment. This paper presents a practical use of new heuristics with the ArcView and solution of address mail for several cities in Slovakia served by Slovak Post ltd. Other Decision Support Systems that solve SRP are presented as TRANSCAD developed by Caliper Corporation or GeoRoute promoted by Canadian Post and GIRO.

Expert Decision-support Systems for Decision-making

1988 ◽  
Vol 3 (2) ◽  
pp. 85-94
Author(s):  
Daniel T. Lee
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Decision Making Process ◽  
Effective Decision ◽  
Effective Decision Support ◽  
Expert Decision Support System ◽  
User Friendly

Computers have made tremendous contributions towards transactional processing. However, the highest pay-off the computer can make is not in transactional processing but in decision-making. Recently, expert systems have just begun to be used in the decision-making process. Individual technologies alone are inadequate for an effective decision support. The purpose of this paper is to investigate the related issues in decision support and to develop an expert decision support system (EDSS) for combining decision support systems and expert systems into a unified whole for decision support. The emphasis will be on developing a DSS/ES model which can be used to integrate the traditional DSS database and ES knowledge-base for building a user-friendly EDSS.

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Sign in / Sign up

Export Citation Format

Share Document