Teadmispõhise tarkvaraarenduse meetodid / Methods of Knowledge Based Software Development 2016

Course code: ITI8600 (Ainekaart eesti keeles ITI8600)

Language: The default language of the course is English, but if all students understand Estonian, it will be in Estonian.

Lecturers:

• Tanel Tammet, tanel.tammet@ttu.ee, 6203457, TTÜ ICT-426
• Juhan Ernits, juhan.ernits@ttu.ee, 6202326, TTÜ ICT-428 (handles ÕIS registrations)

Past editions

Teadmispõhise tarkvaraarenduse meetodid / Methods of Knowledge Based Software Development - 2015

Time, place, result

• Lectures: Mondays 14:00-15:30, CYB-Veenus
• Labs: Fridays 14:00-15:30, ICT-401

Grading

The final grade will be based on 40% of points from homework assignments and 60% of the result of an exam.

There will be four homework assignments, one for each block. Assignments will give up to 10 points each. In order to successfully pass the course, at least three homeworks must be successfully defended.

Homeworks can be done alone or in pairs. Pairs will be formed randomly by the lecturers, separately for each homework. As said, you can always opt to do it alone.

Homework has to be presented during lab time to the lecturer on site: email submissions are not accepted. Both pair members must be present during presentation: in case one of them is not present, the homework of the missing person is not considered to be defended. It is also not guaranteed that both pair members get the same grade.

The homeworks have to be submitted to the university git and then defended: git details will be presented later by Juhan.

Homework deadline policy:

• Defended code must be submitted for defence latest one date before the defence deadline (example: defence deadline 22. Sept, submission 21. Sept).
• In case the homework is defended in time, you have one extra week to add missing details/improvements without losing points.
• In case the homework is not defended in time, you have two extra weeks to defend it, but in this case you will get only half the points.
• No homeworks are accepted after the two extra weeks after the deadline have passed.
• In order to be accepted to exam you have to successfully defend at least three of the four homeworks.

Grades and additional homework info available at https://ained.ttu.ee

Materials for search algorithms

The search algorithms block was based on the following chapters from the book Artificial Intelligence, a Modern Approach, 3rd Edition, by Stewart Russell and Peter Norvig. (The book is available in TUT library as [1] and [2]):

• Chapter 3: Solving problems by searching
• Chapter 4: Beyond classical search
• Chapter 5: Adversarial search
• Chapter 6: Constraint satisfaction problems

In particular, it will be necessary to be able to choose best methods from the ones mentioned in those chapters for solving particular problems. In addition it is necessary to be able to charachterize the properties of these approaches in terms of relevant criteria (branching factor, time complexity, space complexity, completeness).

Course structure

The course will consist of four interconnected blocks covering crucial areas of the subject:

Search algorithms

Homework is available in Moodle. To log in you will need to use your TUT e-mail account in Office 365. The groups will be assigned to participants who are registered to the course in Moodle on September 12. Homework defence deadline: 30. September.

• Tree search, graph search, formulating problems to be solved by search (recap of what you know)
  • The task of the first lab is to pull code from the AIMA Python project and look at the search.py file. You should be able to run the examples shown in the lecture.

• Search 1, Search 2

• Beyond classical search, Constraint solving problems

Homework 1

Note: HW1 hints are available in https://ained.ttu.ee. The groups and git repositories are available.

Git cloning urls are https://youruniid@git.ttu.ee/ained/iti8600/hw1teamXX.git, where XX corresponds to the 2-digit number of your team. All team members have rights to push code to the server.

Knowledge representation

Knowledge representation homework

Homework defence deadline: 3. November. Presentation afer this deadline will give half of the points. Absolute deadline 1 December.

Work should be submitted to git. Latest one day before deadline.

New groups and repositories are available.

Useful in-depth material for reading as free pdf-s:

• Knowledge Representation and Reasoning
• Handbook of Knowledge Representation
• Interesting to browse: recent conference proceedings
• Interesting to browse: course materials, course materials
• Google natural language processing publications

Block structure:

Intro: SQL, logic and RDF.

First lecture as ppt and as as pdf

Natural language and restricted natural language

Useful NLP links and notes

Lecture intro as ppt and as as pdf

See also about IBM Watson

RDFa, RDFs, OWL and rules

Lecture intro as ppt and as as pdf

• Start with the wikipedia take on RDF and RDFs
• RDFa in wikipedia and w3c RDFa primer
• Continue with w3c RDF primer which contains also RDFs aka RDF schema.
• OWL OWL wikipedia, w3c OWL primer

Logic for uncertain knowledge

• default logic: main material for this lecture.
• Uncertain_prob_fuzzy.ppt‎ Intro to probabilistic and fuzzy logic.
• Vienna_tanel_2.pdf Additional examples and combining.
• nonmonotonic logic: long intro to main concepts
• belief and knowledge

Interesting to try out:

• dlv system for answer set programming: usable for implementing default logic

Things we looked at before: