### 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 AND REASONING Code COMP304 Coordinator Dr D Kuijer Computer Science Louwe.Kuijer@liverpool.ac.uk Year CATS Level Semester CATS Value Session 2017-18 Level 6 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 description logic and their use;

• To study epistemic logic and its use

• To study methods for reasoning under uncertainty

• ### Learning Outcomes

be able to explain and discuss the need for formal approaches to knowledge representation in artificial intelligence, and in particular the value of logic as such an approach;

be able to demonstrate knowledge of the basics of propositional logic

be able to determine the truth/satisfiability of modal formula;

be able to perform modal logic model checking on simple examples

be able to perform inference tasks in description logic

be able to model problems concenring agents'' knowledge using epistemic logic;

be able to indicate how updates and other epistemic actions determine changes on epistemic models;

have sufficient knowledge to build "interpreted systems" from a specification, and to verify the "knowledge" properties of such systems;

be familiar with the axioms of a logic for knowledge of multiple agents;

be able to model simple problems involving uncertainty, using probability and decision theory;

be able to demonstrate knowledge of the basics of probability and decision theory, and their use in addressing problems in knowledge representation.

### Syllabus

1. Introduction to knowledge representation (KR), formalisms for KR and in particular propositional logic (1week).
2. 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
3. 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
4. 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.

Lecture -

Tutorial -

### Teaching Schedule

 Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL Study Hours 30 10 40 Timetable (if known) Private Study 110 TOTAL HOURS 150

### Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Unseen Written Exam  150  Semester 1  75  Yes  Standard UoL penalty applies  Final Exam Notes (applying to all assessments) 2 (sets of) assessment tasks - Two class tests of 1 hour duration each to be held in a scheduled lecture or tutorial slot. Written examination
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Coursework  1 hour  Semester 1  12.5  Yes  Standard UoL penalty applies  Class test 1
Coursework  1 hour  Semester 1  12.5  Yes  Standard UoL penalty applies  Class Test 2