Overview of common uses of AI in Automated Decision Support.

1. Artificial Intelligence for
Automated Decision Support
Valerii Klymchuk

2. AI is an area of computer science
Artificial Intelligence is a collection of concepts and ideas for development of
intelligent systems in different areas and domains.
Artificial Intelligence is concerned with 2 basic ideas:
• What is intelligence? (study of human thought process).
• Representation and duplication of the thought processes in machines.
Alan Turing designed a test to determine whether a computer exhibits
intelligent behavior - the Turing Test: “A computer can be considered smart
only when a human interviewer cannot identify the computer while
conversing with both: an unseen human being and an unseen computer.”

3. Abilities considered to be signs of
intelligence:
• Learning or understanding from experience.
• Making sense out of ambiguous or contradictory messages.
• Responding quickly and successfully to a new situation (flexibility).
• Using reasoning in solving problems and directing conduct effectively.
• Understanding and inferring in a rational way.
• Applying knowledge to manipulate the environment.
• Thinking and reasoning, Dealing with perplexing situations.
• Recognizing and judging the relative importance of different elements in a
situation.

4. AI: Field Applications

5. AI: Disciplines

6. Knowledge engineering is a collection of
activities:
• for the acquisition of knowledge from human experts and other
sources, and conversion of this knowledge into a repository.
• to help experts articulate how they do what they do and to document
this knowledge in usable form.
It often applies principles and tools of Artificial Intelligence.

7. Knowledge Engineering in Rule-based System
Two types of rules are common in artificial intelligence:
• Knowledge rules, or declarative rules, state all the facts and relationships
about the problem.
• Inference rules, or procedural rules, offer advice on how to solve a
problem, given that certain facts are known.
Knowledge rules go to the knowledge base, whereas inference rules become
part of the inference engine. The inference engine uses the rules and facts
to draw conclusions. It directs the search through the collection of rules in
the knowledge base (pattern matching). When all the IF parts of a rule are
satisfied, the rule said to be fired. The new knowledge generated by the rule
is inserted into the memory as a new fact. Engine keeps doing so until the
goal is achieved.

8. Knowledge rules might look like:
• Rule 1: IF an international conflict begins, THEN the price of gold goes up.
• Rule 2: IF the inflation rate declines, THEN the price of gold goes down.
Inference (procedural) rules:
• Rule 1: IF the data needed are not in the system, THEN request them from
the user.
• Rule 2: IF more than one rule applies, THEN deactivate any rules that add
no new data.
A set of activities for the acquisition of knowledge from human experts and
other sources, and its conversion into a repository is called Knowledge
Engineering. It applies principles and tools of Artificial Intelligence.

9. The Process
of Knowledge
Engineering

10. Inference Mechanisms (or reasoning) in
Rule-based Systems:
• Backward chaining is a goal-driven approach in which you start from
an expectation of what is going to happen (i.e., hypothesis) and then
seek evidence that supports (or contradicts your expectation. Often,
this entails formulating and testing intermediate hypotheses (or
subhypotheses).
• Forward chaining is a data-driven approach. We start from available
information as it becomes available or from a basic idea, and then we
try to draw conclusions. The ES analyses the problem by looking for
the facts that match the IF part of its IF-THEN rules. As each rule is
tested, the program works its way toward one or more conclusions.

11. • Feedforward (multilayer perceptron with backpropagation).
• Recurrent Networks - a pictoral representation
• Associative Memory
• Kohonen’s Self-Organizing feature Maps
• Hopfield Networks
Artificial Neural Network (ANN)
Architectures:

12. Ultimately, the architecture
of a neural network model
is driven by the task
it is intended to carry out.
Hopfield network

13. Elements of ANN:
• A neural network is composed of
processing elements, organized in
different ways to form the networks
structure.
• The basic processing elements (PE)
of an ANN are artificial neurons.
Each neuron receives inputs,
processes them, and delivers a
single output, as shown. Processing information in an Artificial Neuron.

14. General ANN Learning Process
• In supervised learning, the learning
process is inductive; that is, connection
weights are derived from existing
cases. The usual process of learning
involves three tasks:

15. The information
flows
unidirectionaly
from input layer
to hidden layers
to output layer.
Feedforward (multilayer perceptron with
backpropagation) Network

16. Architecture, where
the connections
between the layers
are not
unidirectional;
rather, there are many
connections
in every direction
between the layers and neurons.
Recurrent Networks - a pictorial
representation
A Recurrent Neural Network Architecture

17. • Provide a way to
represent
multidimensional data in
usually in one or two
dimensions.
• Learn to classify data
without supervision (i.e.,
there is no output
vector).
• Commonly used for
clustering tasks due to
self-organizing capability.
Kohonen’s Self-Organizing Feature Maps
(SOM)
4 x 4 nodes connected to the input layer (with three inputs)
representing a two-dimensional vector

18. Applications. NN models have been used as:
• Classifiers. Typically are multilayer models in which information is
passed from one layer to the next, with a goal of mapping the input
to the network to a specific category, as identified by an output of the
network.
• Forecasting tools.
• Customer segmentation mechanisms.
• General optimizers. In contrast, can be a single layer of neurons,
highly interconnected, and can compute neuron values iteratively
until the model converges to a stable state. This stable state
represents an optimal solution to the problem under analysis.

19. Visual Interactive Models (VIM)
• Systems developed for the military and the video-game industry have
thinking characters who can behave with a relatively high level of
intelligence in their interactions.
The VIM approach can be used in conjunction with Artificial Intelligence.
Integration of the two techniques adds several capabilities that range
from the ability to build systems graphically - to learning about the
dynamics of the system.

20. Case: Predictive integrated modeling and decision
support for Power Generators by StatSoft:
• Optimizes operation Parameters
Problem: A coal-burning 300MW multicyclone unit required optimization for
consistent high flame temperatures to avoid forming slag and burning excess fuel
oil.
Results: After optimizing the control parameters, flame temperatures showed
strong responses, resulting in cleaner combustion for higher and more stable flame
temperatures.
• Predicts problems before they happen
Results: Optimized settings resulted in consistently lower NOx emissions with less
variability and no excursions.
• Reduces Emissions (NOx, CO)
Results: After optimization, NOx emissions under low-load operations were
comparable to NOx emissions under higher loads.

21. Case: Watson
• Facilitates evidence based support at MSKCC.
• It learned the process of diagnosis and treatment through natural language
processing. Was trained to gain knowledge by comparing an individual
patient’s medical information against a variety of treatment guidelines,
published research, and other insights.
• Provides individualized, confidence-scored recommendations to the
physicians. Provides a platform to look at the case from different angles.
• Its voice capabilities allow physician to speak to Watson.
• Watson also Assists the insurance providers in detecting fraudulent claims
• Provides approval for medical treatments based on clinical and patient data
at WellPoint insurance provider.

22. Knowledge-based Management System
• In the medical field amount of medical information doubles every 5 years.
This massive growth limits physician’s decision-making ability in diagnosis
and treatment. Patients histories and electronic medical records can be
analyzed in combination with existing medical knowledge.
• One of the most widely publicized knowledge-based DSS is IBM’s Watson
system. It employs such techniques as: natural language processing;
hypothesis generation and evaluation, evidence-based learning.
• Watson successfully played Jeopargy television show and beat the other
human competitors. Later it evolved into a question-answering computing
platform that is being used commercially in the medical field.

23. Knowledge-driven DSS
(KBDSS) or an Intelligent Decision Support System (IDSS) is a system
that integrates knowledge from experts.
• Involve application of knowledge technologies to address specific
decision support needs.
• Are utilized in the creation of automated decision-making process.
• Rules are used to automate the decision-making process. These rules
are either an expert system (ES) or structured like one. Rule-based
expert systems – a technique developed in the area of artificial
intelligence are the foundation for building KBDSS.
• All intelligence-based DSS fall into this category including ANN and ES.

24. Expert Systems Features, Symbolic Reasoning
Such computer-based information systems use well-stored, organized and quickly retrievable expert
knowledge to improve productivity and quality of performance. ES must have following features:
• Symbolic Reasoning is the basic rationale of Artificial Intelligence. Knowledge must be represented
symbolically, and primary reasoning mechanism must be symbolic, rather then mathematical
calculation. Reasoning mechanisms include backward chaining and forward chaining.
• Expertise. Associated with a high degree of intelligence and vast quantity of knowledge.
• Deep knowledge: knowledge base should not be trivial for non-experts.
• Self knowledge. ES must be able to examine its own reasoning and provide explanations as to why a
particular conclusion was reached. It should be able to learn from its past success and mistakes.
First generation ES use if-then rules to represent and store their knowledge. The second-generation ES
are more flexible in adopting multiple knowledge representations and reasoning methods. They may
integrate fuzzy logic, neural networks, or genetic algorithms with rule-based inference to achieve a
higher level performance.
Expertise is the extensive, task-specific knowledge that an experts possesses.

25. Automated Decision System (ADS or DAS )
• New approach to supporting decision making.
• Attempts to automate highly repetitive decisions based on business rules and are mostly
suitable for frontline employees who must make quick decisions.
• Is a rule-based system that provides a solution in one area to a specific repetitive problem
usually in one industry (e.g., to approve or disapprove a request for a loan; to determine the
price of a store-item, etc.) ADS provide a rule-based solution.
The following are examples of business rules:
• “If only 70 percent of the seats on a flight are sold, offer X discount to nonbusiness travelers,”
• “If an applicant owns a house and makes over $ 100,000 a year, offer a $10,000 credit line.”
Such rules are based on experience or derived through data mining, can be instantly applied to
problems (e.g., “Based on the information provided and subject to verification, you will be
admitted to our university”), or rules can be provided to a human, who makes the final decision.

26. Automated DSS facts:
• Make the decisions in real time or near-real time.
• Almost all airlines use automated DS to assign dynamic prices based
on actual demand. ADS initially appeared in the airline industry
where they were called revenue (or yield) management systems.
• Giant Food Stores worked with DemandTec to deploy a system for its
pricing decisions. The system handles massive amount of point-of-
sale and competitive data to model and forecast demand, handles a
large amount of price changes without increasing staff. Giant’s
productivity has doubled as a result.

27. General Architecture
of Automated Decision Systems

28. References
Business Intelligence and Analytics. Systems for Decision Support, 10th,
Edition, 2015, Sharda, Delen, Turban, Pearson, ISBN: 978-1-292-00920-9