Knowledge Based AI

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> The aim of this course is to provide the conceptual and practical (systems building) foundations for knowledge representation and reasoning in Artificial Intelligence.</div> <wbr style="-webkit-text-stroke-width:0px;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;white-space:normal;word-spacing:0px;">ILO 1: Relate knowledge based approaches to real world applicationsILO 2: Apply resolution-based reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problemsILO 3: Describe the syntax and semantics of first-order logic (and the Datalog and Prolog fragments) and use it to model problemsILO 4: Differentiate between deductive, inductive and abductive reasoning and apply them to perform learning and inference in knowledge based systemsILO 5: Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge basesILO 6: Write Prolog programs to solve automated reasoning tasks and explain how they will executeILO 7: Demonstrate the forward and backward chaining reasoning methods and compare their implementation and practical characteristics (e.g. efficiency, termination)  Analytical skills Problem solving 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. Synchronous Sessions11, 1 x per weekLecture Video material11 hrLaboratories10 hours in total, 5 2-hour sessions. 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 24011COMP24011 is a co-requisite of this course Y Mathematical Logic for Computer Science     Ben-Ari, Mordechai.    Springer London     2012    ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,9781447141297&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">9781447141297</a>Learn Prolog now!     Blackburn, Patrick, 1959-    College    2006    ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,1904987176&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">1904987176</a>An introduction to description logic     Baader, Franz,    Cambridge University Press    2017    ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,9781139025355&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">9781139025355</a>Knowledge representation and reasoning     Brachman, Ronald J.,    Morgan Kaufmann    2004    ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,9781558609327&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">9781558609327</a>Artificial intelligence : a modern approach     Russell, Stuart J.    Pearson    2014    ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,1292024208&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">1292024208</a>Artificial intelligence : a modern approach /    Russell, Stuart J.    Pearson,    2021    ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,9781292401171&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">9781292401171</a> Assessment written exam 2 Lectures 11 Practical classes & workshops 10 0 77 Course unit materials<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.