CCC-KR
Course Title
Knowledge Representation
Course Type
CUGS CS Review / CUGS CS Core / CUGS CS Advanced
/ Other
Periodicity
Once every other year..
Suggested # of Credits
4.5 HE Credits (Higher Education Credits)
Intended audience
The intended audience for this course is all CUGS students
interested in topics in Knowledge Representation with a background in
logic and artificial intelligence.
Course goal
Any software or physical system exhibiting sophisticated or
intelligent behavior requires knowledge about itself and its
competences, knowledge about the environment in which it interacts, and
knowledge
about other agents and their competences. The topic of knowledge
representation covers the ontological issues involved in the modeling
of intelligent artifacts and their embedding environments, the
representation of required knowledge as data structures in a computer,
and its usage by intelligent artifacts, most often in the form of
implicit or explicit inference mechanisms. The goal of this course is
to
provide a framework for understanding different approaches to knowledge
representation, instantiate the framework with a number of existing
approaches to knowledge representation, and to demonstrate the
use of such techniques in and by intelligent artifacts.
Prerequisites
Introductory Course in Artificial Intelligence
CUGS Logic I
Related courses
CUGS Logic I , II, and III. CUGS Core Artificial Intelligence, CUGS
Advanced Artificial Intelligence, CUGS Advanced Knowledge
Representation.
Contents
Please refer to the course goal section for a general content
description. A more specific content description is contained in the
points below:
-
Philosophical, cognitive and computational perspectives
concerning knowledge representation.
-
Modeling intelligent and physical artifacts and their embedding
environments. Distinctions and similarities between quantitative and
qualitative approaches to modeling complex systems.
-
Different approaches to knowledge representation; logic-based
approaches, procedural-based approaches, mixed approaches. Exact versus
inexact inference techniques.
-
Ontological issues; time, space, quantity, quality, epistemic
and ontic properties of agents, approximate vs crisp concepts, etc.
-
Overview of some specific techniques; temporal logics,
description logics, nonmonotonic logics, frames, semantic networks,
inheritance hierarchies, qualitative simulation, production rules, etc.
-
Some applications; WITAS UAV project, logic-based planning;
semantic web.
Organized by
Department of Computer Science, Linköping University
Organization
See course schedule.
Examination
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Literature
Selected articles. Possibly a book.
Detailed reading list: ...
Examiner
Patrick Doherty
Course homepage
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Other information
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