COMP24412
Knowledge Based AI
10
Semester 2
Undergraduate
Level 2
Joseph Razavi
Unit coordinator
Department of Computer Science
1
FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors '
European Credit Transfer & Accumulation System Rating : 5.0
<div> Intelligent systems need to be able to represent and reason about the world. This course provides an introduction to the key ideas in knowledge representation and different types of automated reasoning. The course is a mixture of theoretical and practical work: at the end of the course students will know the principles that such systems use, and they will have experience of implementing those principles in running systems.</div>
<div> Intelligent systems need to be able to represent and reason about the world. This course provides an introduction to the key ideas in knowledge representation and different types of automated reasoning. The course is a mixture of theoretical and practical work: at the end of the course students will know the principles that such systems use, and they will have experience of implementing those principles in running systems.</div><div> </div><div> This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates.</div>
<div> The aim of this course is to provide the conceptual and practical (systems building) foundations for knowledge representation and reasoning in Artificial Intelligence.</div>
<p><wbr style="color: rgb(0, 0, 0); font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;" /></p><div><ul> <li>ILO 1<span style="white-space:pre"> </span>Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge bases</li> <li>ILO 2<span style="white-space:pre"> </span>Describe the syntax and semantics of first-order logic (and the Datalog and Prolog fragments) and use it to model problems</li> <li>ILO 3<span style="white-space:pre"> </span>Demonstrate the forward and backward chaining reasoning methods and compare their implementation and practical characteristics (e.g. efficiency, termination)</li> <li>ILO 4 Apply resolution-based reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems</li> <li>ILO 5 Explain the theoretical limitations of reasoning techniques for (fragments and extensions of) first-order logic</li> <li>ILO 6 Write Prolog programs to solve automated reasoning tasks and explain how they will execute</li> <li>ILO 7 Differentiate between deductive, inductive and abductive reasoning and apply them to perform learning and inference in knowledge based systems</li> <li>ILO 8 Relate knowledge based approaches to real world applications such as (but not limited to) program synthesis or circuit design verification</li></ul></div><p> </p>
Analytical skills
Problem solving
<div><strong>First-Order Logic and Automated Reasoning</strong></div><div>Syntax and Semantics</div><div>Translation to clausal form</div><div>Ordered Resolution</div><div>Saturation based proof search</div><div>Model Construction</div><div> </div><div><strong>Prolog</strong></div><div>Syntax and execution</div><div>Simple logical programs</div><div>Relation to backward chaining with Horn clauses</div><div>Theorem Proving with Prolog</div><div> </div><div><strong>Knowledge Representation</strong></div><div>Ontological Engineering</div><div>Categories and Objects</div><div>Events</div><div>Reasoning Systems for Categories</div><div>Semantic networks</div><div>Description logics</div><div>Reasoning with Default Information</div><div> </div><div><strong>Inductive and Abductive Reasoning</strong></div><div>Learning as a Logic Problem</div><div>Learning with Knowledge</div><div>Inductive Logic Programming</div><div>Explanations and Diagnosis</div><div>Explanation-Based Learning</div><div> </div><div> </div><div> </div><div> </div>
<p><strong><span style="font-size:14px;">Synchronous Sessions</span></strong></p><p>11, 1 x per week</p><p><span style="font-size:14px;"><strong>Lecture Video material</strong></span></p><p>11 hr</p><p><strong><span style="font-size:14px;">Laboratories</span></strong></p><p>10 hours in total, 5 2-hour sessions.</p>
1
Written exam
30%
8
Practical skills assessment
70%
The course has a number of lab exercises which are marked in the lab as usual, and feedback on these exercises is provided by written comments on the work and orally by the marker.
COMP24011
Introduction to AI
Co-Requisite
Compulsory
COMP24412 Pre-requisites 24011<p>COMP24011 is a co-requisite of this course</p>
Y
<p><a href="http://studentnet.cs.manchester.ac.uk/syllabus/index.php?code=COMP24412&year=2020" target="_blank">COMP24412 reading list</a> can be found on the Department of Computer Science website for current students.</p><p><span style="font-size:14px">· Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, 2016. </span></p><p><span style="font-size:14px">· Patrick Blackburn, Johan Bos and Kristina Striegnitz: <a href="http://www.let.rug.nl/bos/lpn/" style="color:#0563c1; text-decoration:underline" title="http://www.let.rug.nl/bos/lpn//">Learn Prolog Now!</a>, College Publications, 2006. </span></p><p><span style="font-size:14px">· Ronald Brachman and Hector Levesque, Knowledge Representation and Reasoning, Morgan Kaufmann, 2004 </span></p><p><span style="font-size:14px">· Dennis Merritt, Building Expert Systems in Prolog, Springer, 1989</span></p>
Assessment written exam
2
Lectures
22
Practical classes & workshops
10
0
77
<p><strong><span style="font-size:14px;">Course unit materials</span></strong></p><p><a target="_blank" href="http://studentnet.cs.manchester.ac.uk/ugt/syllabus.php">Links to course unit teaching materials</a> can be found on the School of Computer Science website for current students.</p>