Chapter 4 Decision Support and Artificial Intelligence Brainpower for Your Business

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Chapter 4Decision Support and Artificial
IntelligenceBrainpower for Your Business
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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.

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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?

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Making decisions by visualizing information as
maps

1. Remember the 4Ps
2. Product
3. Price
4. Promotion
5. Place
6. The where of things

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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

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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

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Decision making may not be linear.
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Decision making may not be linear.
http//www.witiger.com/powerpoints/goinginternati
onal/sld009.htm
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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.

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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 10th anniversary

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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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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

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The decision makersalliance with the DSS
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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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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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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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Components of a DSS
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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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GEOGRAPHIC INFORMATION SYSTEMS

• GPS technology is greatly facilitating the
  ability of GIS to provide helpful info

http//www.witiger.com/ecommerce/mcommerceGPS.htm
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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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Artificial intelligence (AI)

• Prof. John McCarthy , Professor of Computer
  Science at Stanford University

http//www-formal.stanford.edu/jmc/whatisai/node2.
html
Prof. McCarthy (retired) was a famous Computing
Science professor at Stanford University and he
was responsible for the coining of the term
"Artificial Intelligence"
http//en.wikipedia.org/wiki/John_McCarthy_(comput
er_scientist)
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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
http//www-formal.stanford.edu/jmc/whatisai/node2.
html
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Artificial intelligence (AI)
Artificial intelligence types according to Prof.
John McCarthy , Professor of Computer Science at
Stanford University
http//www-formal.stanford.edu/jmc/whatisai/node2.
html
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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Artificial intelligence (AI)

• Artificial intelligence types according to Prof.
  John McCarthy , Professor of Computer Science at
  Stanford University

http//www-formal.stanford.edu/jmc/whatisai/node2.
html
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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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.
http//www-formal.stanford.edu/jmc/whatisai/node3.
html
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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.
http//www-formal.stanford.edu/jmc/whatisai/node3.
html
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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)
http//www-formal.stanford.edu/jmc/whatisai/node3.
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A.I.- Expert Systems
Types of A.I.

• 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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Traffic Light Expert System
Types of A.I.
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Expert systems can
Types of A.I.

• 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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Expert systems cannot
Types of A.I.

• 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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Neural Networks and Fuzzy Logic
Types of A.I.

• 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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Neural Networks Can
Types of A.I.

• 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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Neural Networks
Types of A.I.

• 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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Neural Networks fo real? ?
Types of A.I.

• 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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Fuzzy Logic
Types of A.I.

• 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.

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Fuzzy Logic
Types of A.I.

• handling imprecise or subjective information

http//www.iscid.org/encyclopedia/Fuzzy_Logic
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Fuzzy Logic
Types of A.I.

• 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.

http//www.iscid.org/encyclopedia/Fuzzy_Logic
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Fuzzy Logic
Types of A.I.

• 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.

http//www.iscid.org/encyclopedia/Fuzzy_Logic
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Genetic Algorithms
Types of A.I.

• 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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Genetic Algorithms Can
Types of A.I.

• 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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Intelligent Agents
Types of A.I.

• 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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Information Agents
Types of A.I.

• 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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Monitoring-and-Surveillance Agents
Types of A.I. Types of Intelligent Agents

• Monitoring-and-surveillance (predictive) agents
  constantly observe and report things of interest.
  
• For a computer network, a monitoring-and-surveilla
  nce 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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Data-Mining Agents
Types of A.I. Types of Intelligent 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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User Agents
Types of A.I. Types of Intelligent 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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Multi-agent Systems and Agent-based Modelling
Types of A.I. Types of Intelligent Agents

• 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.

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Agent-Based Modelling
Types of A.I. Types of Intelligent Agents

• 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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Swarm Intelligence
Types of A.I. Types of Intelligent Agents

• 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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