DSS and Expert System.pptx taxonomy classification characteristics components applications

DSS - Taxonomy , classification, characteristics, benefits ,components, architecture, application
Expert system its subtask, components in agriculture, advantages, limitations

1. DECISION SUPPORT SYSTEMS A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance. DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions. Typical information that a decision support application might gather and present are:  inventories of information assets ,  comparative sales figures between one period and the next,  Projected revenue figures based on product sales assumptions. 1 1

2. Taxonomy/Classification of DSS As with the definition, there is no universally-accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler differentiates, • Passive DSS • Active DSS • Cooperative DSS A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.  A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove 2 2

3.  A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.  A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats. A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.  A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analysing a situation 3 3

4. DSS characteristics • DSS are designed specifically to facilitate decision processes. • DSS should support rather than automate decision making. DSS should be able to respond quickly to the changing needs of decision makers. DSSs incorporate both data and models • DSSs objective is to improve the effectiveness of the decisions, not the efficiency with which decisions are being made. • DSSs provide support for decision makers mainly in semi-structured and unstructured situations by bringing together human judgment and computerized information . • DSSs must be designed to interact directly with the decision maker in such a way that the user has a flexible choice and a sequence of knowledge management activities. 4 4

5. Benefits • Improves personal efficiency • Speed up the process of decision making • Increases organizational control • Encourages exploration and discovery on the part of the decision maker • Speeds up problem solving in an organization • Facilitates interpersonal communication • Promotes learning or training • Generates new evidence in support of a decision • Creates a competitive advantage over competition • Reveals new approaches to thinking about the problem space • Helps automate managerial processes 5 5

6. Components of DSSs Database management system (DBMS): A DBMS serves as a data bank for the DSS. It stores large quantities of data that are relevant to the class of problems for which the DSS has been designed and provides logical data structures (as opposed to the physical data structures) with which the users interact. Model-base management system (MBMS): The role of MBMS is analogous to that of a DBMS. Its primary function is providing independence between specific models that are used in a DSS from the applications that use them. The purpose of an MBMS is to transform data from the DBMS into information that is useful in decision making. Dialog generation and management system (DGMS): The main product of an interaction with a DSS is insight. As their users are often managers who are not computer-trained, DSSs need to be equipped with intuitive and easy-to-use interfaces. These interfaces aid in model building, but also in interaction with the model, such as gaining insight and recommendations from it. The primary responsibility of a DGMS is to enhance the ability of the system user to utilize and benefit from the DSS. 6 6

7. Architecture of DSS: 7 7

8. Decision Support Systems in an Agricultural Perspective Application of DSS in the area of agriculture : integrated crop management decision support , encompasses fertilizer management, weed management, water management, plant protection, soil erosion, land use planning, drought management, conservation and improving soil fertility, local water balance, e fficient agronomical practices, canopy management, pest and insect management, reducing pre- and post-harvest losses, conservation of forests and global environment change Web-based DSSs are also playing important role in dissemination of technology transfer related to crop management practices, irrigation scheduling, fertilizer application, etc. 8 8

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10. Expert System: An Expert System (ES), also called a Knowledge Based System (KBS), is a computer program designed to simulate the problem-solving behavior of an expert in a narrow domain or discipline. The expert system could be developed for decision-making and location specific technology dissemination process. An expert system is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. Expert systems helps in selection of crop or variety, diagnosis or identification of pests, diseases and disorders and taking valuable decisions on its management. The expert system which developed earlier were more of text based and could be utilized only by the extension officials and scientists. Considering the importance of ICT enabled interventions in agriculture and providing timely expert advise to farmers, the expert system on agriculture and animal husbandry was proposed. 10 10

11. An Expert System is a computer program that stimulates the judgment and behaviour of a human (or) an organization that has expert knowledge and experience in a particular field. It is program that emulates the interaction a user might have with a human expert to solve a problem. An Expert System is a problem solving and decision making system based on knowledge of its task and logical rules or procedure for using knowledge. Both the knowledge and the logic are obtained from the experiences of a specialist in the area. The Expert System uses a hierarchical classification and a mix of the text description; photographs and artistic pictures. The system involves two main sub tasks: Diagnosis Management The system is designed and developed using Visual Basic as front- end and Microsoft Access as back- end software . 11 11

12. Components of the Expert system in Agriculture: The home page of the expert system has three important components : Information System: Information system is web based static information wherein all the technological and complementary information from A to Z about the crop are pooled and loaded in this component. Decision Support System: Decision support system is a computer-based information system including knowledge based system that support decision making activities. A decision is a choice between alternatives based on estimates of the values of those alternatives. Diagnosing System (Crop Doctor) : Crop doctor is a vital component in the Expert system which acts as artificial intelligence. It is picture and image based “if and then rule’’ based programme which has written using Dot net programme. In crop doctor component of expert system, major pests, diseases and deficiency disorders are included. 12 12

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14. Advantages of Expert System: • The system can be used by extension personnel, researchers and farmers to identify crop diseases and enable to proceed their management. • User can easily identify the disease on the basis of photographs of symptoms and text descriptions of disease. • The user friendly software developed using windowing environment, thus provides enough facilities to identify the disease and to suggest the remedy conveniently. • Provide consistent answers for repetitive decisions, processes and tasks. • Hold and maintain significant levels of information. • Reduce employee training costs. • Centralize the decision making process. • Create efficiencies and reduce the time needed to solve problems. • Combine multiple human expert intelligences. • Reduce the amount of human errors. • Review transactions that human experts may overlook. 14 14

15. Limitations of Expert System: • Many farmers in the country are illiterate and knowledge of computers in rural areas is still unreached. • It needs to be expanded and updated to accommodate new diseases and ailments of important crops in the locality. • There is a need to include other disease diagnosis techniques such as, laboratory tests, soil test report, tissue test, plant analysis report, etc. • If the picture used in expert system is poor quality, the confusion in diagnosis of the problem will be happened and ultimately decision making will not be done properly. Therefore, the picture quality is required to be enriched. • The complexities arising in managing rules for large knowledge base. It is difficult to write knowledge-based rule and place them in proper sequence for larger number of parameters. Verification of large numbers of rule-based system is difficult. • Since the computer is lack of common sense, the programmer should develop the expert system in efficient way. If he or she does mistake, everything will be collapsed. • Will not be able to give the creative responses that human experts can give in unusual circumstances. • Lack of flexibility and ability to adapt to changing environments. • Not being able to recognize when no answer is available. • Knowledge acquisition remains the major bottleneck in applying expert system technology to new domains. • Maintenance and extension of a rule base can be difficult for a relatively large rule base. 15

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