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.

The lectured take place on Tuesday, 2 - 3:45 pm in BIN-2.A.01.

The exam will take place on Tuesday, June 7th, 2011, 2 pm. 

Please note that the official page on the VVZ is relevant with respect to all information retaining exams.

A full syllabus draft will be available as PDF.

Responsible Lecturer:

• Prof. Dr. A. Bernstein

Responsible Assistants:

• Lorenz Fischer
• Cosmin Basca

We will use: S. Russel's & P. Norvig's Artificial Intelligence: A Modern Approach (Third Edition, Pearson).

• The Readings below are listed as the chapters in the third edition!
• Chapters 3 and 4 are available in PDF here.
• You can find a downloadable version of the book here (they offer student discounts).

Some additional papers and books. To be announced!

• Part 0: Introduction
• Part 1: Search & Planning
• Part 2: Logic
• Part 3: Probabilities...
  • Uncertainty
  • Bayesian Networks
    • Knowledge Representation
    • Inference
  • Induction
    • Decision Trees
    • Naïve Bayes (NEW!!!) <-- NOT Part of the Exam!
  • Hidden Markov Models

• Interesting AI Demos and Projects
• Demo Programs/Applets
• Sample Exam

• Assignment 1, Due Date 22nd of Marchh
• Assignment 2, Due Date 12th of April
• Assignment 3, Due Date 17th of May

There will be 3 assignments. Each assignment accounts for 10% of the final grade (all together 30% of your final grade). We expect all students to collect at least half of all the assignment points.