Knowledge-Based Artificial Intelligence

Please note

This document only provides information for the academic year selected and does not form part of the student contract

School:

School of Computing and Engineering

Credit Rating:

Level (including FHEQ):

H (FHEQ Level 6)

Graded or Non Graded:

Graded

Version Valid From:

2023-09-25

Module Leader:

Emmanuel Papadakis

Version Number

2023.02

Learning Methods

Guided Independent Study

Lecture

Practical Classes and Demonstrations

Requirements

Pre-requisites

Synopsis

This module explores industry standard knowledge-based Artificial Intelligence techniques and technologies, as well as current developments in the field. It aims to further develop your skills and knowledge that are gained at intermediate levels of study, to cover advanced techniques such as defeasible reasoning and non-classical planning. You will gain… For more content click the Read More button below.

This module explores industry standard knowledge-based Artificial Intelligence techniques and technologies, as well as current developments in the field. It aims to further develop your skills and knowledge that are gained at intermediate levels of study, to cover advanced techniques such as defeasible reasoning and non-classical planning. You will gain the opportunity to explore relevant applications relying on knowledge-based concepts and techniques such as knowledge graphs and cognitive robotics, as well as recent advances in the field of Artificial General Intelligence.

Learning Strategy

This module will be taught using a mixture of lectures and tutorial/practical sessions. Lectures will deliver the necessary concepts, foundations and principles. Tutorials and practicals will reinforce lecture material and provide students with familiarisation and practical experience on designing and constructing intelligent systems using a variety of industry standard knowledge-based… For more content click the Read More button below.

This module will be taught using a mixture of lectures and tutorial/practical sessions. Lectures will deliver the necessary concepts, foundations and principles. Tutorials and practicals will reinforce lecture material and provide students with familiarisation and practical experience on designing and constructing intelligent systems using a variety of industry standard knowledge-based AI tools and techniques. The University’s Virtual Learning Environment (VLE) will be used to support and supplement lectures, tutorials, practicals and individual learners’ requirements.

Outline Syllabus

• Knowledge-based Systems and Expert Systems• First Order Logic• Constraint Satisfaction Problems• Knowledge Engineering o Acquisition o Representation o Validation o Refinement• Defeasible Reasoning• Case-based Reasoning• Beyond Classical Search• Planning and Actions in the Real World• Cognitive Robotics• Beyond Knowledge-based AI: Artificial General Intelligence

• Knowledge-based Systems and Expert Systems
• First Order Logic
• Constraint Satisfaction Problems
• Knowledge Engineering
o Acquisition
o Representation
o Validation
o Refinement
• Defeasible Reasoning
• Case-based Reasoning
• Beyond Classical Search
• Planning and Actions in the Real World
• Cognitive Robotics
• Beyond Knowledge-based AI: Artificial General Intelligence

Learning Outcomes

On successful completion of this module students will

Analyse thoroughly concepts and techniques relevant to knowledge engineering, representation and reasoning.

Examine the use of knowledge-based AI to determine actions using non-classical search and planning and relevant applications such as robotics.

Demonstrate knowledge engineering skills by representing a particular problem domain and deriving new knowledge through reasoning.

Apply and evaluate critically advanced knowledge-based AI tools and techniques to implement intelligent systems for applications such as robotics.

Formative Assessment

Assessment 1: Other

Description:Feedback to students will be given during tutorial and practical sessions and through the University’s VLE. Various tools and techniques will be examined during tutorial and practical sessions in order for students to test out underlying theories. Feedback and help in using the tools and techniques will enable students to assess their progress and understanding.

Individual or Group:Individual

Summative Assessment

Assessment 1: Portfolio

Description:A report produced individually documenting the process and results of carrying out a knowledge engineering process given a problem domain, acquiring, representing, validating and refining knowledge relevant to the domain and demonstrating the use of the produced knowledge model for reasoning. Deliverables include a report (indicatively 2000 words) and the developed knowledge models.

Assessment Weight:50

Assessment Length:20 Hours (Typical Effort)

Module Learning Outcomes:MLO1, MLO3

Individual or Group:Individual

Final Assessment Component:No

Marked Anonymously:No

Eligible for Tutor Reassessment:Yes

Assessment 2: Portfolio

Description:A portfolio produced individually detailing the implementation of a knowledge-based intelligent system using appropriate techniques such as non-classical search and planning, as well as the results of using the developed system to generate solutions to a real-world problem. Deliverables include a report (indicatively 2000 words) and the code of the implemented system.

Assessment Weight:50

Assessment Length:20 Hours (Typical Effort)

Module Learning Outcomes:MLO2, MLO4

Individual or Group:Individual

Final Assessment Component:Yes

Marked Anonymously:No

Eligible for Tutor Reassessment:Yes

Assessment Criteria

Task 1 will be assessed based on the appropriateness of the knowledge engineering process followed and the quality of the produced knowledge model and accompanying reasoning results. At pass level, the produced model will capture basic knowledge of the domain and reasoning capabilities will be demonstrated. At distinction level, the knowledge model will thoroughly represent the domain in a complete and concise manner, also showing excellent reasoning capabilities capable of deriving meaningful new knowledge.
Task 2 will be assessed based on the effectiveness of the implemented system and the appropriateness of the choices made during the development process. At pass level, a basic working prototype will be delivered, capable of deciding on actions based on available knowledge. At distinction level, a comprehensive knowledge-based intelligent system will be delivered, demonstrating excellence in terms of knowledge representation, reasoning and decision-making.

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