School of Computer Science and Engineering
COMP4418: Knowledge Representation and Reasoning
COMP4418: Knowledge Representation and Reasoning Lecturers:
• Haris Aziz (K17-L3; Haris.Aziz@unsw.edu.au)
• Maurice Pagnucco (Lecturer-in-Charge; J17-501B; morri@cse.unsw.edu.au)
• Abdallah Saffidine (K17-501B; abdallahs@cse.unsw.edu.au) Aim: Introduce
• Techniques used in KR to represent knowledge
• Associated methods of automated reasoning
Units of Credit: 6
Prerequisites: COMP3411 plus 6 Units of Credit in COMP3### Course in AI plus some familiarity with
• LISP/PROLOG
• First-order logic
COMP4418: Knowledge Representation and Reasoning Marking: 3 assignments of equal value (15%) and final exam work 55%.
No project but some programming
Text: References provided in class
Format:
• Lectures:
– Wednesdays 1-3pm, Online
– Thursdays 4-6pm, Online
– Lectures posted online before class. Part of class time used for interactive sessions.
• Consultations: as required
Course Structure:
• 3 weeks: Introduction to KRR.
• 3 weeks: Non-monotonic reasoning, reasoning about action.
• 3 weeks: Social choice, resource allocation and cooperative game theory.
• Note Week 6 is Flexibility Week and there will be no lectures held that week.
Topics for KRR Part 1: Introduction:
– Introduction
– First-order logic
– Expressing knowledge
– Full Clausal logic
– Horn Clause logic
– Procedural representation
– Nonmonotonic reasoning and defaults
Topics for KRR Part 1: Potential Additional Topics:
– Production systems
– Description logics
– Frames
– Inheritance networks
– Probabilities
– Defaults
– Defaults
– Abductive explanation
– Action
– Planning
– Expresiveness/tractability
– Belief Change
– Cognitive Robotics
Topics for KRR Part 2: Non-monotonic reasoning, reasoning about actions
– Introduction to Answer Set Programming
– Solving problems with Answer Set Programming – Reasoning about Actions
Topics for KRR Part 3: Algorithmic Decision Theory
– Social Choice Theory: voting rule; impossibility results; axiomatic approach; tournament solutions; domain restrictions; randomization
– Multi-agent Resource Allocation: allocation problems; efficiency concepts; fairness concepts; representation of preferences; mechanisms; allocation under endowments; allocation under priorities; allocation of divisible items
– Cooperative Game Theory: solution concepts; stability; core, Shapley value, computational of payoffs, computational issues