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Chapter 4 Slide 1 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University.

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Presentation on theme: "Chapter 4 Slide 1 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University."— Presentation transcript:

1 Chapter 4 Slide 1 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Chapter 4 Decision Support and Artificial Intelligence Brainpower for Your Business

2 Chapter 4 Slide 2 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Making decisions by visualizing information as maps A geographic information system (GIS) allows you to see information spatially in the form of a map. The Ice and Marine Services Branch of the Meteorological Service of Canada provides accurate and timely reports on sea ice floes in Canadian waters. The IMSB depends on integrated GIS and other information technologies to acquire and process data from data sources such as satellites, airborne radars and ice/weather models.

3 Chapter 4 Slide 3 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Making decisions by visualizing information as maps 1. Do you use Web-based map services to get directions and find the location of buildings? If so, why? 2. In what ways could real estate agents take advantage of the features of a GIS? 3. How could GIS software benefit a bank wanting to determine the optimal placements for ATMs?

4 Chapter 4 Slide 4 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Making decisions by visualizing information as maps 1. Remember the 4Ps 2. Product 3. Price 4. Promotion 5. Place 1. The where of things

5 Chapter 4 Slide 5 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto How decisions are made One model includes these four phases of decision making: 1. Intelligence – find or recognize a problem, need, or opportunity 2. Design – consider possible ways of solving the problem 3. Choice – weigh the merits and consequence of each solution and then choose one 4. Implementation – carry out the solution

6 Chapter 4 Slide 6 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto How decisions are made Another model called satisficing is simply making a choice even though it may not be the best one. Can be called the just do it model

7 Chapter 4 Slide 7 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Decision making may not be linear.

8 Chapter 4 Slide 8 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Decision making may not be linear.

9 Chapter 4 Slide 9 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Types of Decisions A structured decision uses certain inputs and processes them in a precise way guaranteeing a correct answer e.g. knowing how much GST to charge on a bill. A nonstructured decision involves intuition. No rules or criteria exist guaranteeing choice of the right answer e.g. introduction of a new product line. A recurring decision happens repeatedly. A nonrecurring (ad hoc) decision is made infrequently.

10 Chapter 4 Slide 10 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Types of Decisions A structured decision Example – what is the cost of materials A nonstructured decision Example – will the government continue to subsidize the program A recurring decision Using a particular shipping partner A nonrecurring (ad hoc) decision Caterer for the companys 10 th anniversary

11 Chapter 4 Slide 11 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Decision Support Systems Decision support system (DSS) – a highly flexible and interactive system that is designed to support decision making for a non- structured problem Decision makers are provided with specialized support using IT. They must know what information they need. They must also know how to use the results of the analysis done by the DSS. LO1

12 Chapter 4 Slide 12 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Decision Support Systems Decision support system (DSS) Typical information that a decision support application might gather and present would be: Comparative sales figures between one week and the next Projected revenue figures based on new product sales assumptions The consequences of different decision alternatives, given past experience in a context that is described Eg. Selling 4 for the price of 3, bundling different services

13 Chapter 4 Slide 13 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto The decision makers alliance with the DSS LO1 Page 98

14 Chapter 4 Slide 14 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Three components of a Decision Support System 1. The model management system stores and maintains the DSS models. 2. Models represent events, facts or situations. Businesses use models to represent variables and the relationships between them. For example, a bank could use a model to see what impact various increases to the interest rate would have on their customers mortgage payments. LO1

15 Chapter 4 Slide 15 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Components of a Decision Support Systems 2. The data management component is both the DSS database management system and information from the organization external sources and users. 3. The user interface management component consists of the user interface. This component is where the user inputs information, commands and models into the DSS. LO1 Page 100

16 Chapter 4 Slide 16 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Example of how the three DSS components work together A user communicates needs to the DSS using the user interface management component. For example the user could specify which models to use. Use of the models is provided by the model management component of the DSS. The input for the chosen model(s) is retrieved using the data management component. LO1

17 Chapter 4 Slide 17 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Components of a DSS LO1

18 Chapter 4 Slide 18 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto GEOGRAPHIC INFORMATION SYSTEMS A geographic information system (GIS) is a DSS designed specifically to analyze spatial information. This spatial information can be shown on a map. Businesses use GIS software to analyze information, generate business intelligence, and make decisions. Business geography refers to the use of GIS software to generate maps showing something of interest to the company e.g. maps showing the location of homes for sale. LO1

19 Chapter 4 Slide 19 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto GEOGRAPHIC INFORMATION SYSTEMS GPS technology is greatly facilitating the ability of GIS to provide helpful info LO1

20 Chapter 4 Slide 20 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Artificial intelligence is the use of machines to imitate the way humans think and behave. For example, an insurance company could use AI to detect fraudulent claims. There are four major categories of AI. 1. expert systems 2. neural networks and fuzzy logic 3. genetic algorithms 4. intelligent agents or agent-based technologies LO1 Page 104

21 Chapter 4 Slide 21 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Prof. John McCarthy, Professor of Computer Science at Stanford University LO1 Prof. McCarthy (retired) was a famous Computing Science professor at Stanford University and he was responsible for the coining of the term "Artificial Intelligence"

22 Chapter 4 Slide 22 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Artificial intelligence types according to Prof. John McCarthy, Professor of Computer Science at Stanford University LO1 Logical AI The program decides what to do by inferring that certain actions are appropriate for achieving its goals Search examine large numbers of possibilities, e.g. moves in a chess game Pattern recognition For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. Representation Facts about the world have to be represented in some way. Usually languages of mathematical logic are used Inference For example, when we hear of a bird, we man infer that it can fly

23 Chapter 4 Slide 23 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) LO1 Common sense knowledge and reasoning This is the area in which AI is farthest from human-level Learning from experience The approaches to AI based on connectionism and neural nets specialize in that Planning Planning programs start with general facts about the world (especially facts about the effects of actions) Epistemology This is a study of the kinds of knowledge that are required for solving problems in the world. Ontology Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects Artificial intelligence types according to Prof. John McCarthy, Professor of Computer Science at Stanford University

24 Chapter 4 Slide 24 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Artificial intelligence types according to Prof. John McCarthy, Professor of Computer Science at Stanford University LO1 Heuristics A heuristic is a way of trying to discover something or an idea imbedded in a program refers to experience-based techniques for problem solving a heuristic process may include running tests and getting results by trial and error. As more sample data is tested, it becomes easier to create an efficient algorithm to process similar types of data Genetic programming a technique for getting programs to solve a task by selecting the fittest

25 Chapter 4 Slide 25 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Applications of A.I. according to Prof. John McCarthy, Professor of Computer Science at Stanford University LO1 game playing You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second. speech recognition In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

26 Chapter 4 Slide 26 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Applications of A.I. according to Prof. John McCarthy, Professor of Computer Science at Stanford University LO1 understanding natural language Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains. computer vision The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

27 Chapter 4 Slide 27 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Artificial intelligence (AI) Applications of A.I. according to Prof. John McCarthy, Professor of Computer Science at Stanford University LO1 expert systems A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. heuristic classification An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment)

28 Chapter 4 Slide 28 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto A.I.- Expert Systems An expert (knowledge-based) system is an artificial intelligence system that captures expertise in a certain domain and then applies reasoning capabilities so that a conclusion can be reached. For example, an expert system could be used to diagnose a medical problem. The system could then recommend a treatment for the condition. The expert system is useful because previously medical specialists provided facts and symptoms that were input into the expert system. LO2 Types of A.I.

29 Chapter 4 Slide 29 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Traffic Light Expert System LO2 Types of A.I.

30 Chapter 4 Slide 30 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Expert systems can… Handle massive amounts of information Reduce errors Combine information from many sources Improve customer service Provide consistency in decision making Provide new information Reduce time personnel spend on tasks Reduce cost LO2 Types of A.I.

31 Chapter 4 Slide 31 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Expert systems cannot… Capture expertise if domain experts are unable to explain how they know what they know Be used for reasoning processes that are too complex vague imprecise or require too many rules Use common sense LO2 Types of A.I.

32 Chapter 4 Slide 32 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Neural Networks and Fuzzy Logic A neural network (artificial neural network or ANN) is an artificial intelligence system that is capable of finding and differentiating patterns. For example, bomb detection systems in Canadian airport use neural networks to sense trace elements in the air. LO3 Types of A.I.

33 Chapter 4 Slide 33 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Neural Networks Can… Learn and adjust to new circumstances on their own Participate in massive parallel processing Function without complete or well- structured information Cope with huge volumes of information with many dependent variables Analyze nonlinear relationships LO3 Types of A.I.

34 Chapter 4 Slide 34 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Neural Networks Known as the bottom-up approach to the research and development of intelligent machines, the neural network approach seeks to replicate in a computer the actions and functions of biological neurons found in the human body. Neurons are cellular transmitters of information that work by means of the electrical signals that pass through one neuron to another. A neural network is, therefore, a group of neurons that are connected to each other in complex structures. LO3 Types of A.I.

35 Chapter 4 Slide 35 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Neural Networks – fo real? Two issues are largely responsible for hindering full- scale development of artificial neural networks. Firstly, the construction of neuron simulators is cost- prohibitive. Secondly, current computer architecture still needs more pathways between components. LO3 Types of A.I.

36 Chapter 4 Slide 36 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Fuzzy Logic Fuzzy logic is a mathematical method of handling imprecise or subjective information. It assign values between 0 and 1 to vague or ambiguous information. Rules and processes, called algorithms are constructed. These fuzzy logic algorithms describe the interdependence among variables. For example, fuzzy logic is used by Googles search engine to make sense of the search criteria that was entered. LO3 Types of A.I.

37 Chapter 4 Slide 37 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Fuzzy Logic handling imprecise or subjective information LO3 Types of A.I.

38 Chapter 4 Slide 38 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Fuzzy Logic Fuzzy logic expands traditional Boolean or classical logic in order to allow for partial truths.order Classical logic requires that a concept be deemed either true or false, yes or no, black or white … no allowances for the possibility that the answer may lie somewhere in the middle. Fuzzy logic, on the other hand, is a superset that has been developed to manage the gray areas. LO3 Types of A.I.

39 Chapter 4 Slide 39 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Fuzzy Logic Applications Fuzzy logic normally follows the if/then rules of action and reaction. For example, if a temperature reaches the desired setting, then the thermostat switches itself off. Basic applications of fuzzy logic can be found in a growing number of household appliances such as air conditioners, refrigerators, washing machines, security systems, etc. LO3 Types of A.I.

40 Chapter 4 Slide 40 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Genetic Algorithms A genetic algorithm is an artificial intelligence system that tries to find the combination of inputs that will produce the best solution. LO4 Types of A.I. Genetic algorithms use 1.selection (preference given to better outcomes) 2.crossover (portions of good outcomes are combined in the hope of creating an even better outcome) 3.mutation (randomly try new combinations evaluating each combination)

41 Chapter 4 Slide 41 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Genetic Algorithms Can… Take thousands or even millions of possible solutions, combine and recombine them until it finds the optimal solution Work in environments even if there is no existing model for finding the right solution LO4 Types of A.I.

42 Chapter 4 Slide 42 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Intelligent Agents Intelligent agent – software that assists you, or acts on your behalf, when performing repetitive, computer-related tasks There are four types of intelligent agents: Information agents Monitoring-and-surveillance agents Data-mining agents User or personal agents LO5 Types of A.I.

43 Chapter 4 Slide 43 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Information Agents Information agents are intelligent agents that search for information of some kind and return it to the user. An example is a buyer agent or shopping bot which can help a customer find products or services. When purchasing a book on Amazon.com, a shopping bot displays a list of similar books the customer may be tempted to buy. LO5 Types of A.I.

44 Chapter 4 Slide 44 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Monitoring-and-Surveillance Agents Monitoring-and-surveillance (predictive) agents constantly observe and report things of interest. For a computer network, a monitoring-and- surveillance agent could be used to look for patterns of activity and identify potential problems. Agents could also be used to watch certain Internet sites looking for stock manipulation or insider training. LO5 Types of A.I. – Types of Intelligent Agents

45 Chapter 4 Slide 45 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Data-Mining Agents A data-mining agent is used to discover information in a data warehouse. It must sift through a lot of information. A common data-mining agent looks for patterns in information and categorizes items into classes. For example, a data-mining agent could be used to find investment opportunities in financial markets. LO5 Types of A.I. – Types of Intelligent Agents

46 Chapter 4 Slide 46 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto User Agents User or personal agents are intelligent agents that take action on your behalf. For example, a personal agent could assemble customized news reports to send you. Another example is Movex software which searches the Internet negotiating and making deals with suppliers and distributors. LO5 Types of A.I. – Types of Intelligent Agents

47 Chapter 4 Slide 47 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Multi-agent Systems and Agent-based Modelling By observing parts of the ecosystem, artificial intelligence scientists use hardware and software models to adapt the ecosystems characteristics to human and organizational situations. This is called biomimicry. For example, biomimicry could be used to predict how people will behave under certain circumstances. LO5 Types of A.I. – Types of Intelligent Agents

48 Chapter 4 Slide 48 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Agent-Based Modelling Agent-based modelling – a way of simulating human organizations using many intelligent agents, each of which follows simple rules and adapts to changing conditions Multi-agent system – groups of intelligent agents that can to work independently or interact with each other Air Canada uses agent-based modelling to find the optimal route to send air cargo. LO5 Types of A.I. – Types of Intelligent Agents

49 Chapter 4 Slide 49 Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University 16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto Swarm Intelligence When individuals in a system consistently follow a set of rules, complex collective behaviour may result. Swarm (collective) intelligence is the collective behavior of groups of simple agents that can devise solutions to problems as they come up and eventually develop a coherent global pattern. Swarm intelligence can create and maintain systems that are flexible, robust, decentralized and self-organized. LO5 Types of A.I. – Types of Intelligent Agents


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