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 will be available as PDF.
The lecture takes place on Tuesday, 2 - 4 pm in BIN 2.A.01.
The exam will take place on Tuesday, June 3rd, 2008, 2 pm.
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 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!
|Part 1: Intelligent Search - Problem Solving and Planning as Search, Search||3|
|Di||26.2.||Informed Search||4||A1 out|
|Di||4.3.||Constraint Satisfaction, Adversarial Search||5, 6|
|Part 2: Knowledge intensive processing|
|Di||11.3..||Logic review (Propositional Logic, First Order Logic)||7,8|
|Di||18.3.||Logical Programming||A1 back, A2 out|
|Di||1.4.||Logical Programming, Knowledge Representation||10|
|Part 3: Uncertainty, probability, learning and probabilistic reasoning|
|Di||8.4||Modeling uncertainty - probability revisited||13|
|Di||15.4.||Bayesian Belief Networks||14||A2 back, A3 out|
|Di||22.4.||Bayesian Belief Networks, Probabilistic Reasoning||14|
|Di||29.4.||Reasoning over time - (hidden) Markov models||15|
|Di||6.5.||Induction Part I: Decision Trees|
|Di||13.5.||Induction Part II: Naive Bayes||A3 back|
|Di||27.5.||Questions and answers - Wrap-up|