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CS551J: KNOWLEDGE REPRESENTATION AND REASONING (2018-2019)

Last modified: 22 May 2019 17:07


Course Overview

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.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Old Aberdeen Sustained Study No
Co-ordinators
  • Dr Jeff Pan

What courses & programmes must have been taken before this course?

  • Any Postgraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

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).


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 10 Lectures during University weeks 28 - 29
  • 5 Practicals during University weeks 28 - 29

More Information about Week Numbers


Summative Assessments

Class test (50%);  Project report (50%).

Resit: where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).

Formative Assessment

There are no assessments for this course.

Feedback

Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.

Course Learning Outcomes

None.

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