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Course information Course description Syllabus
Course grades Projects 30% Requirements - Min 7 lab attendances, min 50% of term activity (mid-term ex, projects, lab) - Reading materials. You are expected to do the readings before the class. Books S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach, Prentice Hall, 2009, http://aima.cs.berkeley.edu/
http://www.cs.ubc.ca/~poole/ci.html R. Brachman, H. Levesque. Knowledge Representation and Reasoning. The Morgan Kaufmann Series in Artificial Intelligence, 2004 KRR projects To be announced AI-MAS recommended links See here relevant KRR resources Academic Honesty Policy It will be considered an honor code violation to give or use someone else's code or written answers, either for the assignments or exam tests. If such a case occurs, we will take action accordingly. |
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Professor: Adina Magda Florea
Office hours: Monday 18-19, Tuesday 17-18, Room EF201 |
A course in UPB Master of Science Programme in Artificial Intelligence 2010-2012 |
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This course teaches you models and advanced techniques of knowledge representation and reasoning in AI systems, including models for knowledge representation for the Semantic Web. The course focuses on modern methods and technologies, which allow building „intelligent” programs by representing relevant knowledge of the problem domain and taking optimal decisions towards successful resolution of these problems. Existing representational frameworks developed within AI, their key concepts and inference methods, and relevant applications are presented, to form a coherent picture of what KR is. |
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Topic |
Readings |
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1. General knowledge representation issues (Slides) |
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2. Logical agents – Logical knowledge representation and reasoning |
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First order predicate logic, ATP (Slides) |
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Non-monotonic Logic at SEP, Nonmonotonic Reasoning, Nonmonotonic Reasoning With WebBased Social Networks |
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Modal logic, logics of knowledge and belief
(Slides) |
Modal logic on Wikipedia, Possible Worlds, Belief, and Modal Logic: a Tutorial |
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Semantic networks and description logics,
reasoning services (Slides) |
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Knowledge representation for the Semantic
Web (Slides) |
Description Logics as Ontology Languages for the Semantic Web |
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Midterm exam |
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3. Rule based agents |
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Rete: Efficient
unification |
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The Soar model, universal subgoaling and
chunking (Slides-1, Slides-2) |
A Gentle Introduction to Soar, an Architecture for Human Cognition |
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Modern rule based systems |
Jess, Writing Rules for the Semantic Web Using SWRL and Jess |
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4. Probabilistic agents |
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Probabilistic knowledge representation and
reasoning (Slides) |
Belief Network Inference Algorithms, An Overview of Bayesian Network-based Retrieval Models |
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Rule based methods for uncertain
reasoning |
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5. Intelligence without representation vs. Strong AI |
Class debate |
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Final exam |
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