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Intelligent Systems

Today’s Goals: Intelligent Systems

To become familiar with the distinguishing features of intelligent systems with respect to other software

To become able to appreciate the role of intelligent systems in scientific, business and consumer
To look at several techniques for designing intelligent systems

34.1 (Artificial) Intelligent Systems:

SW programs or SW/HW systems designed to perform complex tasks employing strategies that mimic
some aspect of human thought
One can debate endlessly about whether a certain system is intelligent or not
But to my mind, the key criterion is evolution: it is intelligent if it can learn (even if only a limited
sense) and get better with time
Not a Suitable Hammer for All Nails!

the nature of computations required in a task is not well understood or there are too many exceptions to the rules  or  known algorithms are too complex or inefficient  then  AI has the potential of offering an acceptable solution

Selected Applications:

Games: Chess, SimCity
Image recognition
Medical diagnosis
Business intelligence

Sub-Categories of AI:

Expert systems
Systems that, in some limited sense, can replace an expert
Natural language processing
Teaching computers to understand human language, spoken as well as written
Computer vision

Selected Techniques:

Artificial neural networks
Genetic algorithms
Rule-based systems
Fuzzy logic
Many times, any one of them can solve the problem at hand, but at others, only the right one will do.
Therefore, it is important to have some appreciation of them all .

Neural Networks:

Original inspiration was the human brain; emphasis now on usefulness as a computational tool
Many useful NN paradigms, but scope of today's discussion limited to the feed-forward network, the
most popular paradigm
Feed-forward Network:
It is a layered structure consisting of a number of homogeneous and simple (but nonlinear) processing
All processing is local to a processing element and is asynchronous

During training the FN is forced to adjust its parameters so that its response to input data becomes closer
to the desired response
Based on Darwin's evolutionary principle of ‘survival of the fittest’
GAs require the ability to recognize a good solution, but not how to get to that solution.

Genetic Algorithms (2):

The procedure:
An initial set of random solutions is ranked in terms of ability to solve the problem at hand
The best solutions are then crossbred and mutated to form a new set
The ranking and formation of new solutions is continued until a good enough solution is found or …

Rulebased Systems (1):

Based on the principles of the logical reasoning ability of humans
Components of an RBS:
Working memory
Rule interpreter

The design process:

An RBS engineer interviews the expert to acquire the comprehensive set of heuristics that covers the
situations that may occur in a given domain
This set is then encoded in the form of IF-THEN structures to form the required RBS

34.2 Fuzzy Logic:

Based on the principles of the approximate reasoning faculty that humans use when faced with linguistic
The inputs and outputs of a fuzzy system are precise, only the reasoning is approximate
Parts of the knowledgebase of a fuzzy system:
Fuzzy rules
Fuzzy sets
The output of a fuzzy system is computed by using:
The MIN-MAX technique for combining fuzzy rules
The centroid method for defuzzification
Now we know about a few techniques
Let’s now consider the situation when we are given a particular problem and asked to find an AI
solution to that problem.
How do we determine the right technique for that particular problem?

Selection of an Appropriate AI Technique:

A given problem can be solved in several ways
Even if 2 techniques produce solutions of a similar quality, matching the right technique to a problem
can save on time & resources
Characteristics of an optimal technique:
The solution contains all of the required information
The solution meets all other necessary criteria
The solution uses all of the available (useful) knowledge

How do we determine the suitability of a particular AI technique for a given task. We look at the task’s
requirements and then see which technique fulfils those requirements more completely – the one which
does, is the one we use!
Here are a few aspects of the task and the techniques that we need to be aware off …

Credit Card Issuance:

Challenge. Increase the acceptance rate of card applicants who will turn out to be good credit risks
Inputs. Applicant's personal and financial profiles
Output. Estimated yearly loss if application is accepted
Expert knowledge. Some rules of thumb are available
Data. Profiles & loss data available for 1+ million applicants
Suitable technique?

Determination of the Optimal Drug Dosage:

Challenge. Warn the physician if she prescribes a dosage which is either too high or too low
Inputs. Patient's medical record. Pharmaceutical drug dosage instructions
Output. Warning along with reasons for the warning
Data. Medical records of thousands of patients. Drug dosage instructions on dozens of medicines
Suitable technique?

Prediction of Airline Cabin Crew's Preferences:

Challenge. Predict the future base/status preferences of the cabin crew of an airline. The predicted
preferences will be used by the airline for forecasting its staffing and training requirements
Inputs. Crew's personal profiles. Preference history. Other data.
Output. Predicted preference card for a date one year in the future
Expert knowledge. Some rules of thumb are available
Data. Available for the last four years for 8000 crew members
Suitable technique?

The Right Technique:

Selection of the right AI technique requires intimate knowledge about the problem as well as the
techniques under consideration
Real problems may require a combination of techniques (AI and/or nonAI) for an optimal solution

34.3 Robotics:

Automatic machines that perform various tasks that were previously done by humans
Response speed
Ease of use
Learning curve
Tolerance for complexity
Tolerance for noise in
Tolerance for sparse data
Independence from
Development speed
Computing ease
Pilot-less combat airplanes
Land-mine hunters
Autonomous vacuum-cleaners
Components: Body structure, actuators, power-source, sensors, controller (the AI-based part)

Autonomous Web Agents:

Also known as mobile agents, softbots
Computer program that performs various actions continuously, autonomously on behalf of their
Key component of the Semantic Web of tomorrow
Multi-agent communities are being developed in which agents meet and represent the interests of their
principals in negotiations or collaborations.


Agents of a patient and a doctor get together to negotiate and select a mutually agreeable time, cost

Decision Support Systems:

Interactive software designed to improve the decision-making capability of their users
Utilize historical data, models to solve problems
The do not make decisions - just assist in the process
They provide decision-makers with information via easy to manage reports, what-if scenarios, and
The Future?
Get ready to see robots playing a bigger role in our daily lives
Robots will gradually move out of the industrial world and into our daily life, similar to the way
computers did in the 80’s
Decision support systems will become a bigger part of the professional life of doctors, managers,
marketers, etc
Autonomous land, air, sea vehicles controlled from 1000’s of miles away from the war zone

Today’s Summary:Intelligent Systems

We looked at the distinguishing features of intelligent systems w.r.t. other software systems
We looked at the role of intelligent systems in scientific, business, consumer and other applications
We discussed several techniques for designing intelligent systems

Next Lecture:(Data Management)

To become familiar with the issues and problems related to data-intensive computing
To become able to appreciate data management concepts and their evolution over the years

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