MSc Machine Learning

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.

A basic understanding of sets and relations, e.g., from an undergraduate course unit in discrete algebra.

The unit aims to provide students with a good understanding of logic-based knowledge representation, both in its breadth and depth. This includes both the context of knowledge representation (for example its roles and relations to related subject areas) as well as core technical concepts (for example syntax and semantic of a logic for knowledge representation, reasoning tasks and algorithms, and properties of these algorithms).

A successful student of this unit should be able to

1. Understand and relate the basic concepts in logic-based Knowledge Representation and Reasoning.

2. Discuss the roles of a knowledge base (KB) and their consequences for KB systems.

3. Relate application scenarios or tasks to KR reasoning problems.

4. Explain the trade-offs between expressive power and complexity of reasoning of KR formalisms.

5. Explain relevant properties of reasoning tasks (e.g., soundness, completeness, termination, or complexity) and their relevance for applications of KR formalisms.

6. 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.

7. Apply a suitable reasoning algorithm to solve a reasoning task.

8. Critically assess technologies and analyse their suitability for specific application scenarios

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.

Other (30%) refers to Regular Summative Quizzes
 

There will also be formative coursework for this unit

Students will receive

1. 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.

This is a list indicative of the reading:

Davis, R., Shrobe, H., & Szolovits, P. (1993). What Is a Knowledge Representation?. AI Magazine, 14(1), 17. https://doi.org/10.1609/aimag.v14i1.1029

Selected Chapters of Baader, Franz, et al. Introduction to description logic. Cambridge University Press, 2017.

Selected Section of David Maier, K. Tuncay Tekle, Michael Kifer, and David S. Warren. 2018. Datalog: concepts, history, and outlook. Declarative Logic Programming: Theory, Systems, and Applications. Association for Computing Machinery and Morgan & Claypool, 3–100. https://doi.org/10.1145/3191315.3191317

Brunello, Andrea, Angelo Montanari, and Mark Reynolds. "Synthesis of LTL formulas from natural language texts: State of the art and research directions." 26th International symposium on temporal representation and reasoning (TIME 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2019.

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