Practical work will give 40% and exam 60% of points underlying the final grade.
Practical work will give 40% and exam 60% of points underlying the final grade.
The exam will consist of several small excercises.
The exam will consist of several small excercises.
== Assumed background ==
You should have studied the course [http://lambda.ee/w/index.php?title=Tehisintellekti_ja_masin%C3%B5ppe_alused&oldid=15188 "Basics of AI and Machine Learning"] or get acquinted with the logic and probabilities parts of the course yourself.
== Focus ==
== Focus ==
The main focus of the course is on KR ([https://en.wikipedia.org/wiki/Knowledge_representation knowledge representation]): how to represent nontrivial information in programs and databases, how to build and use indexes for efficient search through large sets of knowledge. Check out [http://www.lisperati.com/tellstuff/ this description].
The main focus of the course is on KR ([https://en.wikipedia.org/wiki/Knowledge_representation knowledge representation]), on a spectrum from simple to very complex: representing and using knowledge in databases, sentences in natural language and commonsense knowledge. A subfocus is on what it takes to build a question answering
system for natural language.
The course contains four blocks built on each other:
The course contains the following blocks:
* Background and basics. Representing simple facts.
* Background and basics. Representing and using simple facts and rules.
* Representing rules.
* Knowledge conveyed by natural sentences: both statistical methods like word2vec and logic-based methods.
* Check out [http://www.w3.org/TR/rdf-schema/ w3c RDF schema]
Choosing the student presentations from:
* [[Nosql links and notes]]
== Lecture block 2: representing rules ==
=== Lecture 5: NELL and nosql databases + intro to the second lab ===
Student lectures on the theme of data and knowledge extraction from the web:
* Nell and systems like Nell (are there any? Check also ConceptNet): Andrei. See some details about [http://rtw.ml.cmu.edu/rtw/ Nell] as a nice example.
* Student presentations chosen from [[Nosql links and notes]]:
** Rait: docs.mongodb.org/manual/ (tehtud)
** Raigo: http://www.neo4j.org/ (tehtud)
Understand RDFS:
Lecture material:
* Continue with [[Media:Kr_rdfs.pdf|RDFS: rdf schema]] and as [[Media:Kr_rdfs.ppt|ppt]]
* [http://attempto.ifi.uzh.ch/site/pubs/ Attempto restricted english] as intro use [http://attempto.ifi.uzh.ch/site/talks/files/Talk.Stanford.05.pdf this talk]
* [http://inform7.com/ Inform] system for interactive fiction, vt ka [http://inform-fiction.org/manual/html/contents.html vanemat raamatut]
Attempto details not necessary for the exam.
== Lecture block 3: time, planning and uncertain knowledge ==
=== Rules in planning and robotics ===
Lecture material:
* [[Media:Kr_rules1.pdf|Rules and logic: refresher]] and as [[Media:Kr_rules1.ppt|ppt]]
* [[blocks world axiom/query examples from lecture]]
See also (not part of exam):
* [[Media:Geoffreasoningnotes.pdf|Geoff reasoning course notes: just for background, no questions in exam]]
* [http://www.ee.pdx.edu/~mperkows/May4/005.Ch11-planning.ppt planning and blocks world]
=== Logic for uncertain knowledge ===
* [http://plato.stanford.edu/entries/logic-nonmonotonic/ nonmonotonic logic] Not necessary for exam.
* [http://en.wikipedia.org/wiki/Default_logic default logic] For exam: main material for default logic.
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* [http://sc.snu.ac.kr/courses/2008/spring/pg/krr/Lectures/Lecture11/Lec11.ppt Reasoning with Conflicting Knowledge] Helpful additional material.
-->
For the exam: you should be able to create and solve small examples with default logic.
Lectures: Wednesdays 12:00-13:30 room U06A-229
Practical work: Wednesdays 14:00-15:30, room ICT-121, ICT-122
Practical work will give 40% and exam 60% of points underlying the final grade.
The exam will consist of several small excercises.
Assumed background
You should have studied the course "Basics of AI and Machine Learning" or get acquinted with the logic and probabilities parts of the course yourself.
Focus
The main focus of the course is on KR (knowledge representation), on a spectrum from simple to very complex: representing and using knowledge in databases, sentences in natural language and commonsense knowledge. A subfocus is on what it takes to build a question answering
system for natural language.
The course contains the following blocks:
Background and basics. Representing and using simple facts and rules.
Knowledge conveyed by natural sentences: both statistical methods like word2vec and logic-based methods.
Ca half of the course themes are covered in this book with freely accessible pdf-s.
The freely accesible pdf of the handbook if knowledge representation gives a detailed and thorough coverage of the subject; far more than necessary for the course.
Observe that a noticeable part of the course contents are not covered by these books: use the course materials and links to papers, standards and tutorials provided.
Practical work
There are three labs: the first two are obligatory, third is optional. The labs have to be presented to the prof and all students present at labwork.
The labs can be prepared alone or by teams of two people.
First lab
Deadline: 16. March (after this there will be a penalty).
