Time: Monday, Wednesday
2:30pm-3:45pm,
Location: Sennott Square, Room 5313
Instructor: Milos
Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at cs pitt edu
office hours: Tuesday 1:00-3:00pm
TA: David Krebs
5324 Sennott Square
e-mail: djk37 at cs pitt edu
office hours: TH 1:15-4:15
Course description
Lectures
Homeworks
Term projects
This introductory knowledge representation course will provide an overview of existing representational frameworks developed within AI, their key concepts and inference methods. The course will cover propositional and first-order logics, their object-oriented extensions (frames), temporal logic and reasoning, inheritance relations, probabilistic models for reasoning and decision making, as well as new topics related to Semantic web and knowledge-based ontologies.
Prerequisites
CS 2710 Foundations of Artificial Intelligence, or equivalent, or the permission of the instructor.
Lectures | Topic(s) | Assignments | |
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August 26 |
Introduction.
Readings: |
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September 3 |
Introduction to LISP.
Readings:
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September 8 |
Introduction to LISP (2nd part)
Readings:
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September 10 |
Propositional logic
Readings:
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Homework assignment 1 | |
September 15 |
Propositional logic II
Readings:
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. | |
September 17 |
Propositional logic: Horn clauses
Readings:
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Homework assignment 2 | |
September 15 |
First order logic
Readings:
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September 24 |
First order logic II
Readings:
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Homework assignment 3 | |
September 29 |
First order logic. Resolution.
Readings:
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October 1 |
First order logic. Efficient inferences.
Readings:
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Homework assignment 4 | |
October 6 |
Production systems. Frame-based representations.
Readings:
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October 8 | Description logic
Readings:
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October 14 |
Hierarchies and inheritance
Readings:
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October 15 | Google talk by Doug Lenat on Cyc project
Readings:
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October 20 |
Planning
Readings:
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October 22 |
Planning II.
Readings:
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Homework assignment 5 | |
October 27 |
Semantic web
Readings:
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October 29 |
Semantic web
Readings:
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November 3 | Midterm exam
Readings:
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November 5 |
Modeling Uncertainty
Readings:
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Homework assignment 6 | |
November 10 |
Bayesian belief networks
Readings:
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November 12 |
Bayesian belief networks: inferences
Readings:
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Homework assignment 7 | |
November 17 |
Bayesian belief networks: inferences
Readings:
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November 19 |
Decision making in the presence of uncertainty
Readings:
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Homework assignment 8 | |
December 1 |
Decision making in the presence of uncertainty II.
Readings:
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December 3 |
Markov decision processes
Readings:
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Homework assignments will include a mix of programming and written problems. Programming assignments will be implemented in the lisp language. The assignments (both written and programming parts) are due at the beginning of the class on the day specified on the assignment. In general, no extensions will be granted. See rules for the submission of programs.
Collaborations:
You may discuss material with your fellow students, but the report and
programs should be written individually.
The term project is due at the end of the semester and accounts for a significant portion of your grade. In very general terms, a project should address a knowledge representation and/or reasoning problem. It may consist of a development of a simple knowledge base (expert system) application, the development of a reasoning, explanation, or consistency checking modules for KBs, or application of KR and reasoning to support advanced web queries.
Students With Disabilities:
If you have a disability for which you are or may be requesting an
accommodation, you are encouraged to contact both your instructor and
Disability Resources and Services, 216 William Pitt Union, (412)
648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS
will verify your disability and determine reasonable accomodations for
this course.