Practical AI (aka Business Intelligence) 2011 - 587


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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.

General Information

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:

Responsible Assistants:


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!

Course Slides


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.



Time Date Subject Readings (Chapters) Assignment
Di 22.2. Introduction 1  
    Part 1: Intelligent Search - Problem Solving and Planning as Search, Search    
Di 1.3. Informed Search 3, 4 A1 out
Di 8.3. Constraint Satisfaction, Adversarial Search 5, 6  
    Part 2: Knowledge intensive processing    
Di 15.3. Logic review (Propositional Logic, First Order Logic) 7,8  
Di 22.3. Logical Programming   A1 back, A2 out
Di 29.3. Logical Programming, Knowledge Representation 12  
    Part 3: Uncertainty, probability, learning and probabilistic reasoning    
Di 5.4. Modeling uncertainty - probability revisited 13  
Di 12.4. Induction Part I: Decision Trees 18.1-18.4 A2 back, A3 out
Di 19.4. Bayesian Belief Networks 14  
Di 3.5. Bayesian Belief Networks, Probabilistic Reasoning 14  
Di 10.5. Reasoning over time - (hidden) Markov models 15  
Di 17.5. Induction Part II: Naive Bayes   A3 back
Di 24.5. NO CLASS!    
Di 31.5. NO CLASS!    
Di 7.6. EXAM