The document discusses decision support systems (DSS), which help executives make better decisions by using historical and current data from internal and external sources. DSS combine large amounts of data with analytical models and tools to provide better information for decision making. The document also describes group decision support systems (GDSS), which are electronic meeting systems that facilitate group collaboration to solve problems. Finally, the document defines intelligent systems as systems that can learn from experiences to improve performance and decision making.

1. DECISION SUPPORT SYSTEMS Presented By: SHILPI JAIN

2. Decision Support Systems (DSS) Introduction The more information you get from external sources, better your decisions will be. Business executives are faced with the same dilemmas while making decisions. For this they need a lot of information from various tools. A decision support system is a way to model data and make quality decisions based upon it. Making the right decision in business is usually based on the quality of your data and your ability to sift through and analyze the data to find trends in which you can create solutions and strategies for. DSS or decision support systems are usually computer applications along with a human component that can sift through large amounts of data and pick between the many choices.

3. Decision Support Systems (DSS) Decision Support Systems (DSS) help executives make better decisions by using historical and current data from internal Information Systems and external sources. By combining massive amounts of data with sophisticated analytical models and tools, and by making the system easy to use, they provide a much better source of information to use in the decision-making process. Decision Support Systems (DSS) are a class of computerized information systems that support decision-making activities. DSS are interactive computer-based systems and subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to successfully complete decision process task.

4. Decision Support Systems (DSS) History Beginning in about 1980 many activities associated with building and studying DSS occurred in universities and organizations that resulted in expanding the scope of DSS applications. These actions also expanded the field of decision support systems beyond the initial business and management application domain. These diverse systems were all called Decision Support Systems. From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. Also, DSS could support operations decision making, financial management and strategic decision-making.

5. Decision Support Systems (DSS) Framework 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: an inventory of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts) comparative sales figures between one week and the next, projected revenue figures based on new product sales assumptions.

6. Decision Support Systems (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 , active , and cooperative DSS .

7. Decision Support Systems (DSS) Classification criteria: Relationship with User

8. Decision Support Systems (DSS) DSS that just collect data and organize it effectively are usually called passive models , they do not suggest a specific decision, and they only reveal the data. An active decision support system actually processes data and explicitly shows solutions based upon that data. 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. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated.

9. Decision Support Systems (DSS) 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 . Using scope as the criterion, Power differentiates enterprise-wide DSS and desktop DSS .

10. Decision Support Systems (DSS) Classification criteria: Mode of Assistance

11. Decision Support Systems (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. 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 analyzing a situation; they are not necessarily data-intensive.

12. Decision Support Systems (DSS) Classification criteria: Scope

13. Decision Support Systems (DSS) An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small system that runs on an individual manager's PC.

14. Decision Support Systems (DSS) Benefits of DSS Improves personal efficiency Expedites problem solving (speed up the progress of problems solving in an organization) Facilitates interpersonal communication Promotes learning or training Increases organizational control Generates new evidence in support of a decision Creates a competitive advantage over competition Encourages exploration and discovery on the part of the decision maker Reveals new approaches to thinking about the problem space Helps automate the managerial processes.

15. GROUP DECISION SUPPORT SYSTEMS (GDSS)

16. Group Decision Support Systems (GDSS) Technical developments in electronic communication, computing, and decision support, coupled with new interest on the part of organizations to improve meeting effectiveness, are spurring research in the area of group decision support systems (GDSS). A GDSS combines communication, computing, and decision support technologies to facilitate formulation and solution of unstructured problems by a group of people. Group Decision Support Systems (GDSS) are a class of electronic meeting systems, a collaboration technology designed to support meetings and group work.

17. Group Decision Support Systems (GDSS) GDSS are distinct from Computer Supported Cooperative Work (CSCW) technologies as GDSS are more focused on task support, whereas CSCW tools provide general communication support. Group Decision Support Systems (GDSS) were referred to as a Group Support System (GSS) or an Electronic Meeting System or Groupware since they shared similar foundations. However today's GDSS is characterized by being adapted for a group of people who collaborate to support integrated systems thinking for complex decision making. Participants use a common computer or network to enable collaboration.

18. Group Decision Support Systems (DSS) Significant research supports the following advantages of GDSS: Adapting human factors for these technologies, Facilitating interdisciplinary collaboration, and Promoting effective organizational learning. More participation Group synergy Automated record keeping More structure in the meeting higher group satisfaction with the meeting process. the new technology has enabled larger groups to meet, resulting in more information, knowledge, and skills that are brought to bear to the task at hand.

19. Group Decision Support Systems (GDSS) Disadvantages of GDSS: Slow Communication: Most people speak much faster than they type, and thus would usually prefer a verbal environment Not all Tasks are Amenable to GDSSs: Group meetings which involve "one-to-many" communication (for example, a leader lecturing to the group) would not benefit from a GDSS. Only those tasks which require group members to exchange ideas or preferences efficiently ("many-to-many") would benefit.

20. INTELLIGENT SYSTEMS (IS)

21. Intelligent Systems (IS) What is intelligence? There are many definitions of intelligence. A person that learns fast or one that has a vast amount of experience, could be called "intelligent". However for our purposes the most useful definition is: the systems comparative level of performance in reaching its objectives. This implies having experiences where the system learned which actions best let it reach its objectives. What is a System? A system is part of the universe, with a limited extension in space and time. What is outside the frontier of the system, we call its environment.

22. Intelligent Systems (IS) Though it is hard to quantify the intelligence of a system, one can certainly recognize the following two extremes in relation to some of the characteristics that it may possess: (a) Low intelligence: Typically a simple system, it has to be \told" everything and needs complete instructions, needs low-level control, the parameters are set, it is usually mechanical. (b) High intelligence: Typically a complex system, it is autonomous to a certain extent and needs few instructions, determines for itself what the goals are, demands high-level control, adaptive, makes decisions and choices, it is usually computerized.

23. Intelligent Systems (IS) Hence Intelligent Systems are those which learn from their past experiences and put this knowledge in current and future decision making. There are many kinds of Intelligent Systems. Such as : Artificial Intelligent Systems, Fuzzy Logic Systems, Expert Systems, Artificial Neural Networks Systems and Genetic Algorithm Systems

24. Intelligent Systems (IS) Artificial Intelligent Systems (Artificial Intelligence) The definitions for what 'Artificially Intelligent' Systems are can be categorized into four classes:

25. Intelligent Systems (IS) Artificial intelligence ( AI ) is the intelligence of machines and the branch of computer science that aims to create it. the field is defined as "the study and design of intelligent agents“. where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy , who coined the term in 1956, defines it as "the science and engineering of making intelligent machines.“ The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens —can be so precisely described that it can be simulated by a machine

26. Intelligent Systems (IS) AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior. It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine. the term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving -- concepts and methods for building programs that reason about problems rather than calculate a solution.

27. Intelligent Systems (IS) Expert Systems An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called knowledge-based or expert systems . Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence.

28. Intelligent Systems (IS) A wide variety of methods can be used to simulate the performance of the expert however common to most or all are: the creation of a knowledge base which uses some knowledge representation formalism to capture the Subject Matter Expert's (SME) knowledge and a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.

29. Intelligent Systems (IS) Fuzzy Logic Systems (Fuzzy Systems) Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems. A fuzzy system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 or 1 (true or false).

30. Intelligent Systems (IS) The term itself inspires a certain skepticism, sounding equivalent to "half-baked logic" or "bogus logic", but the "fuzzy" part does not refer to a lack of rigor in the method, rather to the fact that the logic involved can deal with fuzzy concepts—concepts that cannot be expressed as "true" or "false" but rather as "partially true". Although genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases , fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand.

31. Intelligent Systems (IS) Artificial Neural Networks The technique is rooted in and inspired by the biological network of neurons in the human brain that learns from external experience, handles imprecise information, stores the essential characteristics of the external input, and generalizes previous experience. An artificial neural network (ANN) , usually called "neural network" (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks.

32. Intelligent Systems (IS) It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

33. Artificial Neural Network A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain

34. Intelligent Systems (IS) Genetic Algorithms GAs are probabilistic search techniques loosely based on the Darwinian principle of evolution and natural selection. A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

35. Thank You!  Presented By : SHILPI JAIN