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Artificial Intelligence

Instructor: Professor Danny Kopec

Danny Kopec received as BA in Psychology modified with Mathematics from Dartmouth College in 1975, and a Ph.D. in Machine Intelligence from the University of Edinburgh in Scotland  in 1983 under the mentorship of Donald Michie, a disciple of Alan Turing’s at Bletchley Park, England. His Ph.D. was entitled “Human and Machine Representations” of Knowledge. Professor Kopec has been at been at Brooklyn College and The Graduate Center since 1999.  He has published over 90 academic papers (many of which can be found at in the areas of problem solving, complex system failures, intelligent tutoring systems, computer chess, knowledge representation, computer science education, and software methodology, amongst others.

In 2013 his textbook with Stephen Lucci of The City University, “Artificial Intelligence in the 21st Century” (Mercury Learning, Inc. 590pp) was published and is the basis for this course.  By the time the course starts, a companion book entitled “Artificial Intelligence Problems and Their Solutions” (with Shweta Shetty and Chris Pileggi) is due to be published (also with Mercury Learning, Inc., 300pp) and will be highly recommended to facilitate students’ problem solving.

Professor Kopec also happens to be an International Chess Master and has published seven well-received books and developed eight instructional  dvd’s (totaling 18.5 hours) in this arena.


Artificial Intelligence is a very broad discipline of computer science, and depending on your perspective and research interests you may feel that certain areas are more intrinsic or important to AI than others. In writing the Preface to his text Dr. Kopec came to the conclusion that his perspective of AI is that it is comprised of five elements:  1) People  2) Ideas  3) Methods  4) Systems and 5) Outcomes. An alternate view of AI would not care whether an intelligent system is based on human methods, but evaluates its effectiveness strictly based on performance.

This perspective is precisely the one that will be pursued in the course.

  • Who are the people who have made the most significant contributions to AI and specific areas of AI?

  • What are the ideas that these significant people presented, are known for and should be remembered by? 

  • What are the methods that were developed by these researchers in the “designated” areas of AI?

  • What are the systems that were built?  How did (or do they) perform? What are their costs, strengths, and limitations? Potentials?

  • And finally, how  are the systems being deployed? How have they affected are lives?

Course Topics

Based on experience from teaching with the text so far, we should be able to cover most Chapters in one two-hour lecture. Hence the topics by Chapter are:

  • Chapter 1: History and Overview of AI

  • Chapter 2: Uninformed Search

  • Chapter 3: Informed Search

  • Chapter 4: Search in Games

  • Chapter 5: Logic

  • Chapter 6: Knowledge Representation

  • Chapter 7: Production Systems

  • Chapter 8: Fuzzy Logic

  • Chapter 9: Expert Systems

  • Chapter 10: Neural Networks

I plan to cover the first ten chapters of the text at least partially plus one other chapter from Chapters 11 – 14 (11.Search According to Mother Nature, 12. Natural Language Understanding, 13. Planning, and 14. Advanced Computer Games).

Learning Outcomes

A student who successfully completes the course should:

  • Have a very good idea of the history and range of topics native to the field of AI.  What are areas of AI? Who have been / are the players? What are the outcomes?

  • Understand the difference between Uninformed and Informed Search Techniques (Chapters 2 and 3, respectively) and learn the main techniques that have been developed.

  • See the value of games as a test-bed for heuristic search techniques

  • Understand diverse human problem solving approaches and learn to distinguish and apply  a number of problem solving methods to a number of typical AI problems.

  • Understand the importance of Logic in as a problem solving method in AI

  • Be aware of the importance of knowledge representation in problem solving, and the notions of intensional and extensional approaches, as well as “the human window” – a possible fertile testbed for research.

  • Understand the important role that production systems have played in AI research and development.

  • Be able to demonstrate at least a rudimentary knowledge of how fuzzy logic works and can be applied.

  • Understand  the methods, results, and values of some of the major expert systems developed during the past 30+ years.

  • Understand the basic ideas behind neural networks.

  • Be founded in at least one research topic area from Advanced Search (Chapter 11), Natural Language Understanding (Chapter 12), Planning (Chapter 13), Advanced Computer Games (Chapter 14), Robotics, and Machine Learning.

Course Assessment and Work

  • Midterm (25%)

  • Final Examination (30%)

  • Problem Solving Activity (10 %)

  • Term Paper / Project (20%)

  • Attendance and Participation (10%)

The course will be driven by lectures, PowerPoint slides, discussion, and problem solving activities exhibited by students.  Students will be encouraged to explore experimental approaches that may develop into research topics, particularly through developing a better idea of kinds of solutions satisfy “The Human Window.”