Presentation on theme: "Decision Support and Artificial Intelligence Jack G. Zheng July 11 th 2005 MIS Chapter 4."— Presentation transcript:
Decision Support and Artificial Intelligence Jack G. Zheng July 11 th 2005 MIS Chapter 4
2 Overview Decision support Can computers help people to make decisions? Artificial intelligence Can computers be like human to make decisions?
3 Decision Making 4 general phases of human decision making (Simon 1977): Intelligence (diagnostic) finding needs and problems Design (brainstorm) finding solutions/choices Choice evaluating solutions and pick one Implementation applying the solution Figure 4.2 on Page 181
4 Types of Decision (1) Structured decision There are specific criteria to judge and the answer is certain Example What final letter grade should I give to you? Non-structured decision Criteria are not explicit, or no criteria at all Example How much database material should I cover in CIS2010? Most decisions involves both parts How do I evaluate students performance in the class?
5 Types of Decision (2) Recurring decision Happening repeatedly; decision criteria quite stable Example How much to spend on advertising next month? Nonrecurring/ad hoc decision Happening infrequently; decision criteria may change every time Example Should I buy out my competitor to expand my business?
Decision Support Systems
7 Decision Support System (DSS) is a type of information system designed specifically to help people make (unstructured) decisions
8 DSS Components Figure 4.5 on page 185
9 DSS Components (2) Data management Storing and maintaining data spreadsheet (data file) database data warehouse User interface (UI) management Nice forms to get user input Various visualizations of analysis output Reports, tables, charts, graphs
10 DSS Components (3) Model management Transforming data to decision related information using models A model is a predefined pattern to process data Calculation: what-if models, goal seeking models Statistical models Optimization Comparison Classification Prediction …
11 DSS Types DSS includes many types: OLAP Collaboration systems GDSS (Group DSS) GIS (Geographic Information Systems) SDSS (Spatial DSS) …
12 Geographic Information Systems GIS is designed specifically to work with spatial information to enhance decision making In GIS, various kind of data are visualized with geographical data (maps) Is GIS just a dynamic map system? Most data can be related to geography Population Sales Weather Traffic Crime …
13 How Does GIS Work? GIS visualizes data as layers Attribute data Spatial data Output
14 GIS Sample Applications Dynamic maps Yahoo!Maps, Google Maps and MapQuestGoogle Maps MS Streets and Trips City and regional planning San Francisco Enterprise GIS SimCity An excellent game using GIS
Artificial Intelligence "His love is real. But he is not."
16 Artificial Intelligence AI (or intelligent systems, knowledge systems) is the technology to let computers to imitate human thinking and behavior in some way What is intelligence? Who is intelligent? Understanding Solving problems Learning
17 Where is AI Used in Computing? Speech recognition Natural language understanding Image/vision processing Robotics Data mining (business use)
18 Business AI Applications Most widely used AI applications/techniques in the business world Expert systems Neural network Genetic algorithm Intelligent agents
19 Expert Systems A system that applies reasoning capabilities, as a human expert does, to reach solutions Also called rule-based system (RBS) or knowledge-based system (KBS) A simple example
20 ES Components An expert system, like any information system, consists of information, people and IT components Domain expertise People Domain expert Knowledge engineer User IT component Figure 4.9 on page 198
21 IT Components in ES This is used to enter coded knowledge (rules) Knowledge (rules) are stored here. It stores and provides reasons to every step of reasoning. This is the brain of the ES. It reasons by matching incoming data and stored rules to reach a solution.
22 Expert Systems in Action 1. Rules: a)If age < 25, then loan risk is high b)If annual income < 50k, then loan risk is high c)If loan risk is high, then refuse 4. Gives reason: Age<25 and income <50k high loan risk refuse 3. Pick rules Data: age 21 Matching rule: rule a) Result: high risk Data: income 40k Matching rule: rule b) Result: high risk (New) Data: high risk Matching rule: rule c) Result: refuse loan 2. Incoming data: Age: 21 Annual income: 40k
23 Applications of ES ES is good for diagnostic (whats wrong?) and prescriptive (what to do?) problems Computer or car diagnostic See more demos on Help desk Customer service Technical support
24 Evaluating Expert Systems Benefits Reliable: reduce errors Consistent: provide consistency in decision making Reduce costs and improve productivity … (more in the book) Difficulties and limitations Expertise is implicit: its difficult to explain Modeling process is complex The system cannot learn and adapt to new situations; it has no common sense or judgment Is it really intelligent?
25 Artificial Neural Network ANN is a way to mimic human brain and neurons ANN can be trained to model complex problems and recognize patterns from massive inputs
26 How does ANN Work Depending on how the learning is done Back propagation Needs to be trained It is usually used for prediction For example, to predict transaction fraud or stock performance Self-organizing Self trained It is usually used to classify data For example, to classify customers or web search results A demo of SOM
27 Pros and Cons of ANN Pros Can learn and adjust Can deal with large amount of data Accurate and fast Embeddable Cons Dont ask why – cant explain; or very difficult to explain
28 Genetic Algorithm GA mimics the evolutionary, survival-of-the-fittest process to generate increasing better solutions to a problem It is usually used when A problem does not have solution sets clearly defined by known functions It is impossible to perform an exhaustive search No need to find the best solution
29 GA Concepts Selection Survival of the fittest Crossover Combining portions of good outcomes in the hope of creating an even better outcome Mutation Randomly trying combinations and evaluating the success (or failure) of the outcome
30 How Does GA Work? 1. Randomly throw out a number of solutions (population) as the initial generation 2. Use the fitness function to evaluate each solution Bad solutions are dropped Selection New solutions are added through Crossover Mutation 3. Repeat step 2 on the new population (generation) Until satisfying solutions are found
31 Applications of GA Evaluating GA GA is good for optimization problems, when decision-making involves hundreds or even millions of possible solutions GA does not guarantee the best answer GA may give different solutions Some applications Finding optimal routes for traveling TSP (Traveling Salesman Problem) Finding optimal scheduling of labor Finding optimal stock portfolio combination Strategies for game playing (chess?)
32 Intelligent Agents Agents are software that acts on your behalf to perform repetitive computer-related tasks Also called bot (?) There are many uses of bots Gathering information (search bot) Biding (bid bot) Computer usage assistance (Office Assistant) …
33 Applications of Intelligent Agent User agent, or personal agent – a secretary? Microsoft agent: Chatbot (messenger bot): Buyer agent or shopping bot To gather and compare information of products and services on the web Example:
34 Applications of Intelligent Agent Monitoring-and-surveillance agent, or predictive agent Observe, analyze and report Some applications include Monitoring network security Monitoring list service Watching your competition, bid, … Data-mining agent Software in a data warehouse to analyze data
35 Summary Human decision making is a 4 step process Computers are being designed to support decision making (DSS) GIS to make decisions just like humans (A.I.) Expert systems Neural network Genetic algorithm Intelligent agents
36 Good Resources All about DSS GIS Internet Guide Herbert Simon IBM Deep Blue Intelligent Agents