1. Chapter 4 Decision Support and Artificial Intelligence Brainpower for Your Business

2. 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. Making decisions by visualizing information as maps
Do you use Web-based map services to get directions and find the location of buildings? If so, why?
In what ways could real estate agents take advantage of the features of a GIS?
How could GIS software benefit a bank wanting to determine the optimal placements for ATMs?

4. Making decisions by visualizing information as maps
Remember the 4P’s
Product
Price
Promotion
Place
The where of things

5. How decisions are made
One model includes these four phases of decision making:
Intelligence – find or recognize a problem, need, or opportunity
Design – consider possible ways of solving the problem
Choice – weigh the merits and consequence of each solution and then choose one
Implementation – carry out the solution

6. 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. Decision making may not be linear.

8. Decision making may not be linear.

9. 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. 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 company’s 10th anniversary

11. 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.
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12. 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. The decision maker’s alliance with the DSS
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14. Three components of a Decision Support System
The model management system stores and maintains the DSS models.
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.
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15. Components of a Decision Support Systems
The data management component is both the DSS database management system and information from
the organization
external sources and
users.
The user interface management component consists of the
user interface. This component is where the user inputs information, commands and models into the DSS.
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16. 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.
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17. Components of a DSS
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18. 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.
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19. GEOGRAPHIC INFORMATION SYSTEMS
GPS technology is greatly facilitating the ability of GIS to provide helpful info
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20. 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.
expert systems
neural networks and fuzzy logic
genetic algorithms
intelligent agents or agent-based technologies
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21. Artificial intelligence (AI)
Prof. John McCarthy , Professor of Computer Science at Stanford University
Prof. McCarthy (retired) was a famous Computing Science professor at Stanford University and he was responsible for the coining of the term "Artificial Intelligence"
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22. Artificial intelligence (AI)
Artificial intelligence types according to Prof. John McCarthy , Professor of Computer Science at Stanford University
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
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23. Artificial intelligence (AI)
Artificial intelligence types according to Prof. John McCarthy , Professor of Computer Science at Stanford University
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
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24. Artificial intelligence (AI)
Artificial intelligence types according to Prof. John McCarthy , Professor of Computer Science at Stanford University
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
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25. Artificial intelligence (AI)
Applications of A.I. according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
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.
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26. Artificial intelligence (AI)
Applications of A.I. according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
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.
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27. Artificial intelligence (AI)
Applications of A.I. according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
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)
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28. Types of A.I.
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.
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29. Traffic Light Expert System
Types of A.I.
Traffic Light Expert System
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30. Expert systems can… Handle massive amounts of information
Types of A.I.
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
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31. Expert systems cannot…
Types of A.I.
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
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32. Neural Networks and Fuzzy Logic
Types of A.I.
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.
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33. Types of A.I.
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
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34. Types of A.I.
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.
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35. Neural Networks – fo real? 
Types of A.I.
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.
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36. Types of A.I.
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 Google’s search engine to make sense of the search criteria that was entered.
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37. Fuzzy Logic handling imprecise or subjective information Types of A.I.
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38. Types of A.I.
Fuzzy Logic
Fuzzy logic expands traditional Boolean or classical logic in order to allow for partial truths.
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.
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39. Fuzzy Logic Applications
Types of A.I.
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.
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40. Types of A.I.
Genetic Algorithms
A genetic algorithm is an artificial intelligence system that tries to find the combination of inputs that will produce the best solution.
Genetic algorithms use
selection (preference given to better outcomes)
crossover (portions of good outcomes are combined in the hope of creating an even better outcome)
mutation (randomly try new combinations evaluating each combination)
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41. Genetic Algorithms Can…
Types of A.I.
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
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42. Types of A.I.
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
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43. Types of A.I.
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.
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44. Monitoring-and-Surveillance Agents
Types of A.I. – Types of Intelligent Agents
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.
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45. Types of A.I. – Types of Intelligent Agents
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.
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46. Types of A.I. – Types of Intelligent Agents
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.
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47. Multi-agent Systems and Agent-based Modelling
Types of A.I. – Types of Intelligent Agents
Multi-agent Systems and Agent-based Modelling
By observing parts of the ecosystem, artificial intelligence scientists use hardware and software models to adapt the ecosystem’s 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.
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48. Agent-Based Modelling
Types of A.I. – Types of Intelligent Agents
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.
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49. Types of A.I. – Types of Intelligent Agents
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.
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