Over the past years, the appearance of applications requiring or benefiting from (classical) artifical intelligence has accelerated. For example, electronic markets for the buying and selling of goods and services over the Web is a fast-growing, multi-billion-dollar segment of the world economy. Knowledge-based techniques for product recommendation, auctions, need identification, vendor selection, negotiation, agent communication, ontologies, business rules, and information integration are of rising interest and have started having practical impact on real Web e-markets.

This class covers the foundational theories (mostly) from the field of (classical) artificial intelligence that have made it possible to evolve to more “intelligent” applications. It will cover areas such as knowledge representation and reasoning (increasingly important through the semantic web effort of the w3c), problem solving, planning, and reasoning under uncertainty. For each of the subjects it will cover the underlying theories and provide a insight into practical applications using those techniques

Please note that though this class does not attempt to answer the grand old question of artificial intelligence: how to build an artificial intelligence. Its goal is to present methods found during this quest that have been surprisingly useful in practical applications.

A full syllabus draft is available as PDF.

Responsible Lecturer:

• Prof. Dr. A. Bernstein

Responsible Assistants:

• Jiwen Li
• Christoph Kiefer

We will use: Russel, S., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice-Hall.

Please note: The second edition of the book (published this year is vastly improved). There are some new chapters, that will be available in the library as we need them.

The Readings below are listed as the chapters in the second edition! A conversion table for the chapters can be found here.

Some of the new chapters in the second edition are available on-line! 

Some additional papers and books. To be announced!

Required: Finished Grundstudium

Of advantage:

• KV Datenbanksysteme
• KV Global verteilte und dynamische Anwendungssysteme

Time Table

To be revised!

Slides

• Part 1: Search, etc.
• Introduction, Planning & Search
• Constraint Satisfaction Problems (CSP) (session 2)
• Games
   
• Part 2: Knowledge Intensive Processing
• Logic and First Order Logic
• Reasoning and Knowledge Representation
   
• Part 3: Learning
• Introduction and Decision Trees
• Naïve Bayes
• Bayesian Belief Networks
• HMMs, etc. - Probablistic models of Time
• Learning BBNs and HMMS

Assignments

• Assignment #1: Search, etc.
  Sample Solutions, puzzle_reolon.jar, puzzle_krist.jar
   
• Assignment #2: Search in Games & Knowledge Representation
sample solution: Part 1, Part 2a, Part 2b, Part 2c
 
  Note for Part 1:
  1) If the game reaches the same board status (A and B on same fields as before) but it is a different player's turn to move, this is definitively a different game status and the corresponding node in the decision tree has to be expanded.
  2) This does not occur in the given assignment with n=6.
  3) We think that point 1) may not occur for any n>2 and can be shown by induction (we omit the proof here).
 
• Assignment #3: Learning

• Interesting AI Demos and Projects

• Comments on Readings

• Demo Programs/Applets

• Exam 2004