Download Artificial Intelligence, Neural Nets and Applications

Indian Institute of Management Bangalore
PGSM II Quarter 2003
Artificial Intelligence, Neural Nets and Applications (AINNA)
Instructor: Rahul De’, Ph.D.
Introduction: Resurgence of AI
After years in the wilderness, the term “artificial intelligence” seems poised to make a
comeback—The Economist, March 14, 2002.
The AI-development community has generated techniques that are beginning to show
promise for solving real business problems involving complex data in dynamic
environments—McKinsey Quarterly, Number 2, 2002.
With the commercial success of technologies such as speech recognition, automated mail
sorting and baggage-handling, online bidding and quote-generation, for example, AI
(Artificial Intelligence) has (re-) emerged as a discipline with a promise and many
potential product offerings. Media hype and blue-sky dreams from amateur
commentators put AI under a cloud in the 90’s when the promise of immensely
intelligent computers that could solve practically any problem did not materialize.
Researchers and developers persevered, regardless, and technologies such as Expert
Systems, Neural Networks and Intelligent Agents, among others, became established as
reliable and capable of solving limited though hard problems.
The objective of this course is to overview the field of AI; to understand its
history, its various branches, and to examine, in detail, certain techniques that have wide
applications for decision making in industrial and service settings. One part of the course
will cover readings in the history of AI and the controversies and debates that have
shaped its growth. The other part of the course will cover readings in AI techniques such
as search, planning, machine learning, neural networks, genetic algorithms and multiagent modelling. The emphasis, in this part of the course, will be on techniques that have
applications in solving significant business problems.
Topics
1.Origins of AI
a.“History of AI,” Chapter 1, Russell and Norvig, 2003.
Additional Reading: “Computing Machinery and Intelligence” by A.M.
Turing, 1950.
2. Symbolic Processing and Intelligent Agents
a.“Computer Science as Empirical Inquiry: Symbols and Search”, Newell and
Simon, 1976.
b.“Intelligent Agents”, Chapter 2, Russell and Norvig, 2003.
3. Problem Solving and Search
a.“Solving Problems by Searching,” Chapter 3, Russell and Norvig, 2003.
4. Planning: basic theory and applications
a.“Planning,” Chapter 11, Russell and Norvig, 2003.
5. Neural Networks: basic theory
a.Chapter 4 from Machine Learning, T. Mitchell, 1997.
6. Neural Networks: applications
a.Decision making
b.Financial applications
7. The AI debates
a.“Artificial Minds,” by Stan Franklin, 1995, 2 chapters.
Additional Reading: “Minds, Brains, and Programs,” by John R. Searle,
1980.
“Intelligence without Representation,” Rodney Brooks, 1991.
8. Machine Learning
a.Induction Learning:Chapter 3 from Machine Learning, T. Mitchell, 1997.
b.Reinforcement Learning:Chapter 13 from Machine Learning, T. Mitchell,
1997.
9.Evolutionary Systems: Genetic Algorithms
a.Chapter 9 from Machine Learning, T. Mitchell, 1997.
b. Fuzzy Systems.
10.Artificial Life systems
a. “Swarm Intelligence: A whole new way to think about business,” by E. Bonabeau
and C. Meyer, Harvard Business Review, 2001.
Grading
Class Participation
20%
Project One, Group
25%
Project Two, Group
25%
Final Exam
30%
Course Requirements
Reading material is provided and students will have to come prepared to each class
for discussions.
Project One: In this first group project, you will have to use Neural Network
software to do the following:
1) estimate a known function,
2) make projections with time-series data, and
3) use a face recognition neural net to identify the members of your group.
You will be required to train a network to complete all these tasks. Details will be
provided in class.
Deadlines: Submission of trained network – October 31, 2003.
Project Two: In this project each group has to research a particular technology for
which a commercial or educational product is available. They have to implement this
technology to solve a sample decision problem, obtained from within their
organization, and explore its ramifications as a general solution. As such this project
will consist of two parts:
1)Download a software and implement a sample problem on it; (60% weight)
2)Research a business problem that can be solved with this technology. (40%
weight)
To ensure variety I will encourage each group to seek a technology that none of the
others is researching; in cases of conflict I will be the arbiter. Details required in the
project report will be provided in class.