Comp 304 & Comp 521: Knowledge Representation and Reasoning (2017-2018)

Lecture Timetable:

Monday 12:00-13:00 (ASHT-LR)

Thursday 14:00-15:00 (BROD-106)

Friday 11:00-12:00 (CHAD-BARK)

Tutorial Timetable (Group 1):

Thursday 13:00-14:00 (BROD-406/4-6a)

Friday 12:00-13:00 (BROD-702)

Tutorial Timetable (Group 2):

Wednesday 12:00-13:00 (MATH-117)

Friday 16:00-17:00 (JSM(bdg. 232)-SR4)

Slides:

• 1. Introduction and Propositional Logic [Full][Handout]

  2. Modal Logic [Full][Handout][Completeness Full][Completeness Handout][Extra Material]

  3. Description Logic [Full][Handout][Consistency Checking Example][Another Example]

  4. Epistemic Logic [Full][Handout]

  5. Probability Theory [Full][Handout]

The slides come in two versions: the full version is the one I use during the lecture, including animations. The handout version does not include the animations.

Exercises:

• 1. Exercises about Propositional Logic [with solutions]

  2. Exercises about Modal Logic [with solutions]

  3. Exercises about Description Logic [with solutions][more exercises][more solutions]

  4. Exercises about Epistemic Logic [with solutions][more exercises][more solutions]

  5. Exercises about Probability Theory [with solutions][more exercises][more solutions]

Class Tests:

• 1. Class Test 1 [with solutions]

  2. Class Test 2 [with solutions]

Last Year's Exams:

• 1. COMP304[solutions]

  2. COMP521[solutions]

This Year's Exams:

• 1. COMP304 [solutions]

  2. COMP521 [solutions]

Lecture streams:

Whenever possible, recordings of the lecture will be made available here. Barring technical difficulties the recordings will also appear on VITAL.

Module Aims (304 & 521):

• • 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 (304 & 521):

At the end of the module, the student will:

• • 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 concerning 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 demonstrate knowledge of the basics of probability and decision theory, and their use in addressing problems in knowledge representation;

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

  • be able to use tableau based methods to do inference in description logic.

Learning Outcomes (521 only):

The student will:

• • be able to perform simple Hilbert-style deductions in modal and epistemic logic;