Intelligent Decision Support Methods.ppt

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1、Intelligent Decision Support Methods,From the book-Intelligent Decision Support Methods by Vasant Dhar and Roger Stein,Intelligent DSS by HCH,2,Information Systems,“I know of no commodity more valuable than information.”Management Information System (MIS) Transaction Processing Systems Accurate Reco

2、rd Keeping Decision Support Systems (DSS) Model-Driven DSS Data-Driven DSS,Intelligent DSS by HCH,3,Intelligence Density,DEF: A Metric for Knowledge Work Productivity.Knowledge Intensive organizations transform raw data into something useful-knowledge-and deliver the knowledge to the part of the org

3、anization where it can be used most effectively.Intelligence Density: How quickly can you get the essence of the underlying data from the output?,Intelligent DSS by HCH,4,The Vocabulary of Intelligence Density,Quality of Model Accuracy, Explainability, Speed, Reliability Engineering Dimension Flexib

4、ility, Scalability, Ease of Use,. Quality of Available Resource Learning Curve, Tolerances for Noise, Complexity,. Logistical Constraints Independence from Experts, Computational Ease, Development Time,Intelligent DSS by HCH,5,Intelligent DSS by HCH,6,Intelligent DSS by HCH,7,Intelligent DSS by HCH,

5、8,Intelligent DSS by HCH,9,Dimensions of Problems and Solutions,Intelligence Density Dimensions: Quality of SystemsHow Well is the System Engineered?Quality of Available ResourcesLogistical Constraints,Intelligent DSS by HCH,10,Intelligence Density Dimensions: Quality of Systems (1/2),Accuracy measu

6、res how dose the outputs of a system are to the correct or best decision. Can you be confident that the errors(results that are not accurate)are not so severe as to make the sys-tem too costly or dangerous to use?Explainabilitv is the description of the process by which a conclusion was reached. Sta

7、tistical models explain the output to some degree in the sense that each independent variable influences or explains the dependent variable in that it accounts for some portion of the variance of the dependent variable.,Intelligent DSS by HCH,11,Other systems, where rule-based reasoning is involved,

8、 show exp1icitly how conclusions are derived, yet others, such as neural networks, generate opaque mathematical formulas. These are sometimes referred to as black boxes, because for the user they are the mathematical equivalent of the magicians black box: Data go in at one end and results come out t

9、he other, but you cannot (easily) see the rationale behind the conclusion. Response speed is the time it takes for a system to complete analysis at the desired level of accuracy. The flip side to this dimension is confidence in the sense that you can ask how confident you are that a certain period o

10、f time, within which the system must provide an answer, will be sufficient to perform the analysis. In applications that require that results be produced within a specified timeframe, missing that time frame means that no matter how accurate and otherwise desirable the results are, they will be usel

11、ess in practice.,Intelligence Density Dimensions: Quality of Systems (2/2),Intelligent DSS by HCH,12,How Well is the System Engineered? (1/3),Scalability involves adding more variables to the problem or increasing the range of values that variables can take. For example, scalability is a major issue

12、 when youre interested in going from a prototype system involving 10 variables to one with 30 variables. Scalability can be a real problem when the interactions among variables increase rapidly in unpredictable ways with the introduction of additional variables(making the system brittle)or where the

13、 computational complexity increases rapidly. Compactness refers to how small (literally, the number of bytes) the system can be made.Once a system has been developed and tested, it needs to be put into the hands of the decision makers within an organization. It must be taken out into the field, be t

14、hat the shop floor, the trading floor, or the ocean floor.,Intelligent DSS by HCH,13,How Well is the System Engineered? (2/3),Flexibility is the ease with which the relationships among the variables or their domains can be changed, or the goals of the system modified. Most systems are not designed t

15、o be used once and then thrown away. Instead they must be robust enough to perform well as additional functionality is added over time. In addition, many of the business processes that you might model are not static (i.e., they change over time). As a result, the ability to update a system or to hav

16、e the system adapt itself to new phenomena important. Embeddability refers to the ease with which a system can be coupled with or incorporated into the infrastructure of an organization. In some situations, systems will be components of larger systems or other databases. If this is the case, systems

17、 must be able to communicate well and mesh smoothly with the other components of the organization infrastructure. A system that requires proprietary software engineer,or specific hardware will not necessarily be able to integrate itself into this infrastructure.,Intelligent DSS by HCH,14,How Well is

18、 the System Engineered? (3/3),Ease of use describes how complicated the system is to use for the businesspeople who will be using it on a daily basis. Is it an application that requires a lot of expertise or training, or is it something a user can apply right out of the box?,Intelligent DSS by HCH,1

19、5,Quality of Available Resources,Tolerance for noise in data the degree to which the quality of a system, most notably its accuracy, is affected by noise in the electronic data. Tolerance for data sparsenessis the degree to which the quality of a system is affected by incompleteness or lack of data.

20、 Tolerance for complexity is the degree to which the quality of a system is affected by interactions among the various components of the process being modeled or in the knowledge used to model a process. Learning curve requirements indicate the degree to which the organization needs to experiment in

21、 order to become sufficiently competent at solving a problem or using a technique.,Intelligent DSS by HCH,16,Logistical Constraints,Independence from experts is the degree to which the system can be designed, built, and tested without experts. While expertise is valuable, access to experts within an

22、 organization can be a logistical nightmare and can be very expensive. Computational ease is the degree to which a system can be implemented without requiring special-purpose hardware or software. Development speed is the time that the organization can afford to develop a system or, conversely, the

23、time a modeling technology would require to develop a system.,Intelligent DSS by HCH,17,Topics,Data-Driven Decision Support Evolving Solutions: Genetic Algorithms Neural Networks Rule-Based Expert Systems Fuzzy Logic Case-Based Reasoning Machine Learning,Intelligent DSS by HCH,18,Data-Driven Decisio

24、n Support,OLTP: On-Line Transaction Processing ISAM: Indexed Sequential Access Method, early DBMS RDBMS: Relational Database Management Systems Data Normalization SQL: Sequential/Structured Query Language EIS: Executive Information Systems Friendly & Intelligent User-interface Data Warehousing and O

25、LAP: On-Line Analytical Process LAN: Local Area Network Data Loader-Converter-Scrubber-Transformer-Warehouse-OLAP,Intelligent DSS by HCH,19,Intelligent DSS by HCH,20,Intelligent DSS by HCH,21,Intelligent DSS by HCH,22,Intelligent DSS by HCH,23,Evolving Solutions - Genetic Algorithms (I),Optimization

26、 Problems: A set of problem variables A set of constraints A set of objectives Example: ACME Transport, Inc., a shipping firm, needs to plan a delivery route that will minimize the time and cost of the shipping, but at the same time , make delivers to all 10 of its overseas clients. Exhaustive Searc

27、h: evaluate all possible 10! = 3,628,800 routes. Problem: If the number of clients increase to 25, then there are 25! = 1.55*1025 possible route. Therefore it will take a very fast computer (evaluate a million route per second) to evaluate only 0.23% possible route in 4 billion years. Often not a LP

28、 problem,Intelligent DSS by HCH,24,The Example - Genetic Algorithms (II),Possible constraints to the ACME problem Shipping costs must be less than 70% of fee charged. Customer waiting time must be less than 90 days. If a customer does more than $x of business with ACME then waiting time must be less

29、 than 60 days.Possible objectives to the ACME problem Overall delivery time is minimized. Overall profit is maximized. Ship fleet wear is minimized. Number of repeated country visits is minimized.,Intelligent DSS by HCH,25,Intelligent DSS by HCH,26,The Origin - Genetic Algorithms (III),GAs were orig

30、inally developed by computer scientist John Holland in the 1970s as experiments to see if computer programs could evolve in a Darwinian sense.GAs are very useful for solving classes of problems that were previously computationally prohibitive, especially in the area of optimization.GAs is a heuristi

31、c techniques that cannot guarantee optimal solutions. Only near optimal solutions can be expected,Intelligent DSS by HCH,27,The Theory of Evolution - Genetic Algorithms (IV),Basic Concept: Natural Selection, i.e., SurvivalDifferent kromes will survive based on the compatibility of their attributes w

32、ith their environment. They are hunted by their predators at night.Each type of krome represents one solution to the survival problem. Kromes with better attributes have higher probability to survive and therefore reproduce,Intelligent DSS by HCH,28,Intelligent DSS by HCH,29,Introduction - Genetic A

33、lgorithms (V),The smallest unit of a GA is called a gene, representing a unit of information in your problem domain.A series of these genes, or a chromosome, represents one possible complete solution to the problem.A decoder converts the chromosome into a solution to the problem. (or interprets the

34、meaning of a chromosome)A fitness function then is used to determines which chromosome solutions are good and which are not very good.,Intelligent DSS by HCH,30,Introduction - Genetic Algorithms (VI),A GA randomly creates an initial population of chromosomes and evaluates their fitness.A new generat

35、ion (new population of chromosomes) is created by combining and refining the information in the chromosome using Selection Crossover MutationThe process is repeated until a satisfactory solution is found.,Intelligent DSS by HCH,31,Intelligent DSS by HCH,32,Intelligent DSS by HCH,33,Intelligent DSS b

36、y HCH,34,Intelligent DSS by HCH,35,Intelligent DSS by HCH,36,Intelligent DSS by HCH,37,Intelligent DSS by HCH,38,Intelligent DSS by HCH,39,Notes - Genetic Algorithms (VII),Do not guarantee an optimal solution.You can use a GA to solve problems that you dont even know hoe to solve. All you need to be

37、 able to do is describe a good solution and provides a fitness function that can rate a given chromosome.How good a solution provided by a GA is determined by how good the problem is formulated.,Intelligent DSS by HCH,40,Simulating the Brain to Solve Problems - Neural Networks (I),Learning preserves

38、 the errors of the past, as well as its wisdom.The Learning Process: Induction Data Generalization ModelThe Example: Over the years, you must have a very good idea how much time you need to spend on and how to prepare a quiz to get certain grade. That is, you build mental models based on the past ex

39、periences (data) by generalization.,Intelligent DSS by HCH,41,The Origin - Neural Networks (II),Neural networks were first theorized as early as the 1940s by two scientists at the University of Chicago (McColloch and Pitts). Works was done in the mid-1950s as well (McCarthy 1956; Rosenblatt 1957) wh

40、en researchers developed simple neural nets in attempts to simulate the brains cognitive learning processes.ANNs are simple computer programs that build models from data by trial and error.Very useful in modeling complex poorly understood problems for which sufficient data can be collected.,Intellig

41、ent DSS by HCH,42,Nervous Systems - Neural Networks (III),Our nervous systems consist of a network of individual but interconnected nerve cells called neurons.Neurons can receive information (stimuli) from the outside world at various points in the network.The information travels through the network

42、 by generating new internal signals that are passed from neuron to neuron. These new signals ultimately produce a response.A neuron passes information on to neighbor neurons by firing or releasing chemicals called neurotransmitters.,Intelligent DSS by HCH,43,Nervous Systems - Neural Networks (IV),Th

43、e connections between neurons at which information transfers are called synapses.Information can either excite or inhibit neurons.Synaptic connection can be strengthened (learning) or weakened (forgetting) over time with experience.With repeated learning, one can generalize his/her experience, modif

44、ying the response to stimuli, and thus ultimately reach the level of reflexes.,Intelligent DSS by HCH,44,Introduction - Neural Networks (V),ANN involves a system of neurons (or nodes) and weighted connections (the equivalent of synapses) inside the memory of a computer.Nodes are arranged in layers:

45、Input layer Hidden layer Output layerThrough learning (trial and error, propagating, other algorithms), ANN adjusts the weights on each connections to match the desired response (minimize the amount of error).,Intelligent DSS by HCH,45,Intelligent DSS by HCH,46,Intelligent DSS by HCH,47,Intelligent

46、DSS by HCH,48,Intelligent DSS by HCH,49,Intelligent DSS by HCH,50,Intelligent DSS by HCH,51,Intelligent DSS by HCH,52,Intelligent DSS by HCH,53,Training Steps of a Neural Network (1/2),Step l: The network makes a guess based on its current weights and the input data. Step 2: The net calculates the e

47、rror associated with the output (at the out,put node). For example, if the desired output were 1, but the network output were 0, the error would be +1, based on the difference between l and 0. Step 3: The net determines by how much and in what direction each of the weights leading in to this node ne

48、eds to be adjusted. How? This is accomplished by calculating how much each of the individual weighted inputs to the node contributed to the error,given the particular input value. So, for example, if a nodes output were too small, the net might need to concentrate on (that is,increase) small or nega

49、tive weights that lead up to that node. In essence, the network feeds back the information about how well its doing to the neurodes in the net, and where possible problems might be.,Intelligent DSS by HCH,54,Training Steps of a Neural Network (2/2),Step4: The net adjusts the weights of each node in

50、the layer according to the analysis in the previous step. For example, in the case where thc output was too small, the neural network will try to increase the values of the positive weights since that would make the weighted sum larger. This would bring the output closer to 1, which is what you want

51、 in this case. Similarly, the neural net should also try to decrease the size of the negative weights (or even make them positive). Step5:The net repeats the process by performing a similar set of calculations (Step l-Step3)for-each node in the hidden layer below it. But since you cannot tell the net what the desired output of each of the hidden nodes should be (they are internal and hidden), the neural network does a kind of sensitivity analysis to determine how large the error of each of these nodes is,