Course details
Code
CS-567
Name
Knowledge Representation and Reasoning
Program
Postgraduate
Areas
Multimedia Technology
Information Systems and Human-Computer Interaction
Description
Textbooks: the course will partially cover material from the following books (in addition to a number of scientific articles):
R. Brachman and H. Levesque, Knowledge Representation and Reasoning. Morgan Kaufmann, 2004,
F. van Harmelen, V. Lifschitz, B. Porter, Handbook of Knowledge Representation, Elsevier, 2008
J. Halpern, Y. Moses, M. Vardi, Reasoning about Knowledge, MIT Press, 2005
H. Levesque and G. Lakemeyer, The Logic of Knowledge Bases, MIT Press, 2000
Aim: The course aims to teach the theoretical background of knowledge representation and to familiarize students with methods of automated reasoning. The course relies on logic to unfold the spectrum of methodologies for knowledge representation and reasoning, covering a wide range of techniques for monotonic and non-monotonic reasoning, reasoning about knowledge, belief and uncertainty, as well as the relation between knowledge and causality. Students will learn to use in practice state-of-the-art logic programming tools that implement formalisms, such as Prolog and Answer Set Programming.
Student Evaluation: It will be based on atomic and group exercises (theoretical and programming), assigned throughout the whole duration of the course. Apart from the implementation, exercises will also require a report.
Topics: 1st-order Logic, resolution-based theorem proving, SAT solvers, Non-monotonic reasoning, Knowledge and Belief, Answer Set Programming, Knowledge and Action, Uncertainty, Commonsense Reasoning, Temporal Reasoning, Planning, Knowledge-Based Systems, Multi- Agent Systems, Cognitive Robotics, Qualitative Reasoning and Diagnosis, Knowledge Engineering, Semantic Web
ECTS
6
Prerequisites
CS-380, CS-387
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