Knowledge Representation
CUGS CS Review / CUGS CS Core / CUGS CS Advanced / Other
Once every other year..
4.5 HE Credits (Higher Education Credits)
The intended audience for this course is all CUGS students interested in topics in Knowledge Representation with a background in logic and artificial intelligence.
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.
Introductory Course in Artificial Intelligence
CUGS Logic I
CUGS Logic I , II, and III. CUGS Core Artificial Intelligence, CUGS Advanced Artificial Intelligence, CUGS Advanced Knowledge Representation.
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.
Department of Computer Science, Linköping University
See course schedule.
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Selected articles. Possibly a book.
Detailed reading list: ...
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