Comp 304 & Comp 521: Knowledge Representation and Reasoning (2016-2017)

Lecture Timetable:

Monday, 15:00-16:00, BROD-107

Tuesday, 10:00-11:00, LIFS-SR2

Wednesday, 09:00-10:00, BROD406/406a

Tutorial Timetable:

Tuesday, 15:00-16:00, BROD-107 (group 1)

Thursday, 12:00-13:00, BROD-402 (group 2)

Slides:

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

  2. Modal Logic [Full][Handout]

  3. Description Logic [Full][Handout][Additional 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. Propositional Logic

  2. Modal Logic

  3. Description Logic

1. Epistemic Logic

   1. Probability Theory

Tests and Exams:

• 1. Class Test 1 [model solution]

  2. Class Test 2 [model solution]

  3. Mock Exam [Model Solution]

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