Last modified: 25 Sep 2019 09:58
Recent advances in AI have changed the perception of what machines can do, from on-line search to answering questions. An underlying feature of many AI systems concern how knowledge is acquired, represented, and reasoned with. Companies such as Google, IBM, and Facebook have been developing sophisticated tools for knowledge representation and reasoning. This module provides the theory and practice of knowledge representation and reasoning, also presenting cutting-edge technologies, libraries and tools. At the end of the course students will be able to design, implement and evaluate knowledge-intensive AI systems.
|Session||Second Sub Session||Credit Points||15 credits (7.5 ECTS credits)|
The course will present the theory and practice of Knowledge Representation and Reasoning. An underlying feature of many AI systems concern how knowledge is represented and the mechanisms to reason with and about this knowledge. Students attending this course are expected to acquire a good understanding of the logical foundations and applications of Knowledge Representation and Reasoning, as well as to become familiar with current bottlenecks and related solutions in the field, including the problem of construction of formal knowledge bases from informal ones (such as those written in natural language). There will be an emphasis on representation and reasoning which is practical and efficient, exploring and incorporating recent technologies as well as working hands-on with state-of-the-practice in knowledge representation and reasoning technologies to build AI systems (e.g., solutions for question-answering, reasoning, Semantic Web, Internet of Things and Big Data).
Information on contact teaching time is available from the course guide.
|Assessment Weeks||Feedback Weeks|
There are no assessments for this course.