Logics for Knowledge Representation and Reasoning

COMP64401 Logics for Knowledge Representation and Reasoning 15 Semester 1 Postgraduate Taught Level 6 Uli Sattler Unit coordinator Department of Computer Science 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 The unit provides an introduction to the basic concepts of Logics for Knowledge Representation and Reasoning, and discusses these in the context of different knowledge representation formalisms and their applications. Knowledge Representation and Reasoning is concerned with the development of suitable formalisms and tools to capture rich domain knowledge (e.g. in bio-health, medicine, and material science) in a machine-processable way. This includes the design of Knowledge Representation (KR) languages - which are usually based on logic - as well as as the development of related reasoning algorithms and the investigation of the underlying reasoning problems. This course unit will provide a solid understanding of a selection of logics underlying Knowledge Representation and Reasoning in knowledge bases: we start with a discussion of knowledge – in contrast to data – and its role in information systems. Then we introduce a basic logic, propositional logic, as well as a range of extensions that have been developed for different reasons and applications. For each of the logics, we discuss what we can say in it (syntax), what this means (semantics), relevant reasoning problems and algorithms (how to get meaning out of a knowledge base or answer queries against this knowledge base), properties of these problems and algorithms (e.g., correctness, complexity), and what this means for their usage in knowledge representation. In this course unit, you will gain a good understanding of logic and its usage in knowledge representation and reasoning.  We talk about syntax, semantics, reasoning tasks and algorithms to solve these tasks, as well as their usage in applications. ILO 1: Apply a suitable reasoning algorithm to solve a reasoning task.ILO 2: Understand and relate the basic concepts in logic-based Knowledge Representation and Reasoning.ILO 3: Relate application scenarios or tasks to KR reasoning problems.ILO 4: Explain the trade-offs between expressive power and complexity of reasoning of KR formalisms. ILO 5: Critically assess technologies and analyse their suitability for specific application scenarios. ILO 6: Explain relevant properties of reasoning tasks (eg soundness, completeness, termination, or complexity) and their relevance for applications of KR formalisms.ILO 7: Discuss the roles of a knowledge base (KB) and their consequences for KB systems.ILO 8: Read and write statements in a KR formalism, i.e., faithfully formulate statements as axioms of a KR formalism and explain the meaning of such statements.  Analytical skills Group/team working Oral communication Problem solving The unit aims to provides an introduction to the basic concepts of Logics for Knowledge Representation and Reasoning and discusses these in the context of different knowledge representation formalisms and their applications.Knowledge, in contrast to data, comes with meaning and the ability to derive new knowledge from the given data. A wide range of logic-based knowledge representation formalisms have been developed and this unit will introduce students to the concept of knowledge representation as well as to some prominent underlying logics, the related reasoning problems and algorithms, and relevant properties of these. In particular, starting from propositional logic, we will discuss a basic Description Logic and the ontology language OWL, the rule-based logic Datalog, and the temporal logic LTL used for automated verification of systems. 1. Introduction (Course Mechanics, 5 roles of KR, KR and related subject areas)2. Propositional Logic (syntax, semantics, entailments, reasoning & application tasks, reasoning algorithm, limitations)3. The Description Logic EL and the OWL 2 EL ontology language (syntax, semantics, entailments, Open World Assumption, reasoning & application tasks, reasoning algorithm, limitations & extensions)4. Datalog language (syntax, semantics, entailments, Closed World Assumption, reasoning & application tasks, relation to EL, reasoning algorithm, limitations & extensions)5. LTL - a temporal logic (syntax, semantics, entailments, reasoning & application tasks, reasoning algorithm, limitations & extensions) We will use blended learning: Synchronous activities include in-person workshops, focusing on discussion of examples, clarifications and Q&A. Labs allow for exploration of coursework, what is expected and how to go about doing it. Asynchronous learning material will be made available in the form of videos and directed reading, as well as formative and summative exercises delivered via the VLE. 0 Other 30% 1 Written exam 70% Other (30%) refers to Regular Summative Quizzes  There will also be formative coursework for this unit Students will receive1. Immediate feedback to auto-graded questions from their weekly quizzes; cohort-level feedback will be provided in the workshops.2. Cohort-level feedback will be provided to formative coursework; individual feedback will be provided on request in the labs.3. Cohort-level feedback will be provided to the exam after marking. A basic understanding of sets and relations, e.g., from an undergraduate course unit in discrete algebra. Y Logic for computer science and artificial intelligence Caferra, Ricardo, ISTE 2011 ISBN: <a href="https://www.librarysearch.manchester.ac.uk/discovery/search?query=isbn,contains,9781118604182&search_scope=MyInst_and_CI&sortby=rank&vid=44MAN_INST:MU_NUI&lang=en&mode=advanced&offset=0" target="_blank">9781118604182</a>An introduction to description logic [electronic resource] 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>What Is a Knowledge Representation? Davis, Randall ; Shrobe, Howard ; Szolovits, Peter The AI magazine 1993 DOI: <a href="https://doi.org/10.1609/aimag.v14i1.1029" target="_blank">10.1609/aimag.v14i1.1029</a>Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom    Deslauriers, Louis ; McCarty, Logan S. ; Miller, Kelly ; Callaghan, Kristina ; Kestin, Greg    Proceedings of the National Academy of Sciences - PNAS    2019    DOI: <a href="https://doi.org/10.1073/pnas.1821936116" target="_blank">10.1073/pnas.1821936116</a> Assessment written exam 2 Practical classes & workshops 22 Supervised time in studio/wksp 40 Work based learning 40 0 46 Additional information on Scheduled activities hours:There will also be a Exam Revision Session (1 hour) for this unit Additional information on Independent study hours:Independent watching videos – 30 hours  Independent reading and working through material – 30 hours  Formative Coursework - 20 hours  Summative Quizzes – 20 hours  Exam Preparation – 24 hours