Sample Solutions for the Assignments are online.
Final Exam Date and Location
The final exam will be taken in BIN 2.A.01 at 2:00pm on Tuesday, June 19, 2007.
Introduction
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
Literature
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!
Prerequisites
Required: Finished Grundstudium
Of advantage:
- KV Datenbanksysteme
- KV Global verteilte und dynamische Anwendungssysteme
Slides
- Part 0: Introduction
- Part 1: Search & Planning
- Part 2: Knowledge Based Systems
- Part 3: Uncertainty
- Section 1: Probability
- Section 2: Learning
- Leraning and Naïve Bayes
- Learning Trees
- Text Ming & Information Retrieval (no slides - blackboard only)
- Section 3: Probabilistic Knowlwdge Representation
Assignments
Time Table
|
Time |
Date |
Subject |
Readings (Chapters) |
Assignment |
|
Di |
20.3. |
Introduction |
1 |
|
|
|
|
Part 1: Intelligent Search |
|
|
|
|
|
Problem Solving and Planning as Search, Search |
3 |
|
|
Di |
27.3. |
Informed Search |
4 |
A1 out |
|
Di |
3.4. |
Constraint Satisfaction, Adversarial Search |
5, 6 |
|
|
|
|
Part 2: Knowledge intensive processing |
|
|
|
Di |
10.4. |
Logic review (Propositional Logic, First Order Logic) |
7,8 |
|
|
Di |
17.4. |
Building a knowledge base, Ontologies |
10 |
|
|
Di |
24.4. |
Inference, Expert Systems, Knowledge Representation |
10 |
A1 back, A2 out |
|
|
|
Part 3: Uncertainty, probability, learning and probabilistic reasoning |
|
|
|
Di |
8.5. |
Modeling uncertainty - probability revisited |
13 |
|
|
Di |
15.5. |
Learning Introduction, Naive Bayes, Tree inducers |
18.1-18.3 |
|
|
Di |
29.5. |
Probabilistic Reasoning: Bayesian Belief Networks |
14 |
A2 back, A3 out |
|
Di |
12.6. |
Reasoning over time - (hidden) Markov models |
15 |
A3 back |
|
Di |
19.6. |
Final exam |
|
|