Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Knowledge Representation
Code COMP521
Coordinator Dr LB Kuijer
Computer Science
Louwe.Kuijer@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2020-21 Level 7 FHEQ First Semester 15

Aims

To introduce Knowledge Representation as a research area.
To give a complete and critical understanding of the notion of representation languages and logics.
To study modal logics and their use.
To study description logic and its use.
To study epistemic logic and its use.
To study methods for reasoning under uncertainty


Learning Outcomes

(LO1) Demonstrate a critical understanding of the languages of modal and description logics by translating between English and those languages.

(LO2) Exhibit a comprehensive understanding of the semantics of modal and description logics by arguing whether formulas of propositional, modal and description logic are true or valid.

(LO3) Analyse scenarios involving knowledge, and represent them in modal and description logics.

(LO4) Have a deep understanding of formal proof methods and apply them to modal and description logics.

(S1) Problem Identification

(S2) Critical Analysis

(S3) Solution Synthesis

(S4) Evaluation of Problems and Solutions


Syllabus

 

Introduction to knowledge representation (KR), formalisms for KR and in particular propositional logic (1 week).
Introduction to modal and description logics (5 weeks): Modal logics: Syntax, semantics (Kripke models), model checking, theorem proving. Description logics: Syntax, semantics, satisfiability checking, expressive description logics.
Applications of modal logic: epistemic logic (3 weeks): One agent case: S5 models, specific properties; Multi-agent case: Modelling epistemic puzzles, reasoning about other's knowledge and ignorance, alternating bit protocols; Group notions of knowledge: Distributed knowledge, common knowledge,examples; Computational models: Interpreted systems.
Handling uncertain information through probability and decision theory (2 weeks): Sample spaces; independence; conditional probability; prior and posterior probabilities; random variables; decision theory for agent systems; Bayesian networks.


Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided

Teaching Method 2 - Tutorial
Description:
Attendance Recorded: Not yet decided

Teaching Method 3 - Assessment
Description:
Attendance Recorded: Not yet decided
Notes: One exam and two class tests


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 30

  10

    5

45
Timetable (if known)              
Private Study 105
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
(521) Final Exam There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Semester 1  150 minutes.    70       
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
(521.1) Class Test 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Around week 5  1 hour for all CAs    15       
(521.2) Class Test 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Around week 10  1 hour for all CAs    15       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.