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
Time |
Subject |
|
Readings (Chapters) |
Assignment |
Mo |
20.10. |
Introduction |
|
|
|
|
Intelligent search |
|
|
|
|
Heuristic Methods, Constraint Satisfaction |
3 & 4 |
|
Mo |
27.10. |
Scheduling, Routing and Games |
5 & 6 |
A1 out |
|
|
Knowledge intensive processing |
|
|
Mo |
3.11. |
Logic review (Propositional Logic, First Order Logic) |
skim 7, 8 |
|
Mo |
10.11. |
Building a knowledge base, Ontologies |
9 & 10 |
A2 back |
Mo |
17.11. |
Inference, Expert Systems |
|
A2 out |
Mo |
24.11. |
Automated Contracting, Semantic Web, Guest: Klaus Bena - EDS |
|
|
|
|
Learning |
|
|
Mo |
1.12. |
Tree inducers |
18 (18.1, 18.2, 18.3) |
|
Mo |
8.12. |
Bayesian Methods |
13 (20.1, 20.2) |
. |
Mo |
15.12. |
Spam filtering, fraud detection, house price estimation |
|
A2 back |
|
|
X-Mass break |
|
|
|
|
Probabilistic learning & reasoning |
|
|
Mo |
5.1. |
Bayesian Belief Networks – Clippy and Co., |
14 (in particular beginning < 14.5) |
A3 out |
Mo |
12.1. |
Hidden Markov Models – Speech Recognition |
We skipped this subject! |
|
|
|
Natural Language Processing |
|
|
Mo |
19.1. |
Text Mining, Information Extraction |
|
A3 back |
Mo |
26.1. |
Machine Translation |
|
|
Mo |
2.2. |
Summary session |
|
|
Mo |
9.2. |
Final exam |
|
|
Handouts
Slides
- Part 1: Search, etc.
- Introduction, Planning & Search (slides for first two sessions)
- Constraint Satisfaction Problems (CSP) (seesion 2)
- Games
- Part 2: Knowledge Intensive Processing
- Logic, First Order Logic
- FOL Reasoning, Logic Programming, and Knowledge Representation
NEW: fixed PDF - Part 3: Learning
- Part 4: Probabilistic learning and Reasoning
- Natural Language Processing
- Text Mining, Information Extraction
- Machine Translation (Jekat)
- Machine Translation II (Röthlisberger)