CSE 591: Knowledge Representation and Reasoning (Fall, 2009)

Syllabus

Time and Place: MW 3:30-4:55 p.m., BYAC 240

Instructor: Joohyung Lee (joolee (at) asu (dot) edu)

Instructor's Office Hours: MW 5-6 p.m. and by appointment, BY 574

Description: Knowledge representation and reasoning is one of the fundamental areas in Artificial Intelligence. Any intelligent agent needs to know in order to behave intelligently, and draw conclusions effectively from the knowledge. Thus the KRR research is concerned with how to encode knowledge in an adequately expressive formalism and how to draw relevant conclusions efficiently from the knowledge base. Various methods have been developed in the past 50 years, and it's often discovered that they are in fact closely related to each other. This course which will introduce basic and recent developments in the research in knowledge representation and reasoning.

Slides

1. General Introduction
2. Introduction

Handouts

1. Review: Classical Logic
2. Introduction to Answer Set Programming
3. Methodology of Answer Set Programming
4. Exercises: Nonmonotonic Causal Theories

Participation

• 1.1: James.E.Holmes
• 1.4: Mike Bartholomew
• 1.5: Ryan Sullivan
• 1.6: Hongxin Hu
• 1.7: Shashvata Sharma
• 1.8: Marcos Alvarez Gonzalez
• 2.1: (Homework)
• 2.3: Gregory Gelfond
• 2.5: Michael Casolary
• 2.6: (Homework)
• 2.7: Jonathan Hwang
• 2.10: (Instructor)
• 2.11: (Instructor)
• 2.12: Hongxin Hu
• 2.14: Mike Bartholomew
• 2.17: Jun Shen
• 2.18: Hongxin Hu (0.5), Ryan Sullivan (0.5)
• 3.5, 3.6, 3.7: (Homework)
• 4.1: Marcos Alvarez Gonzalez
• 4.2: Tylar Hoag
• 4.3: Tuan Nguyen
• 4.5: Tylar Hoag
• 4.6: Tylar Hoag
• 4.7: Tuan Nguyen
• 4.8: Shashvata Sharma
• 4.9: (in class)
• 4.10: Marcos Alvarez Gonzalez

Papers

• V. Lifschitz, L. Morgenstern and D. Plaisted, Knowledge representation and classical logic , in Handbook of Knowledge representation, Elsevier, 2008.
• E. Giunchiglia, J. Lee, V. Lifschitz, N. McCain and H. Turner, Nonmonotonic Causal Theories, Artificial Intelligence, 153(1-2):49-104, 2004.

Classes

• 8.24
  General Introduction to the course. Discussed the policy and the contents of the course
  Reading : The Handbook Ch1 Sec 1.1, 1.2
• 8.26
  Covered the syntax and the semantics of propositional logic
  Supplementary slides
• 8.31
  The syntax first-order logic, informal semantics of first-order logic
  Problems discussed : 1.4, 1.5
• 9.2
  Formal semantics of first-order logic, Herbrand models
  Problems discussed : 1.1
  Supplementary slides
• 9.9
  Compactness Theorem, Transitive clousure
  Problems discussed : 1.7
  
• 9.14
  First Midterm
  
• 9.21
  Grounding
  Problems discussed: 1.6
  Supplementary slides
• 9.30
  choice rules, constraints, cardinality expressions
  
• 10.5
  Theorem on Constraints, cardinality expressions
• 10.7
  N Queens, Schur Number
• 10.12
  Multi-valued propositional formulas
• 10.14
  Sudoku discussion, brief introduction to CCalc
  
• 10.21
  NMCT (Sec 2.3)
  
• 10.26
  Elaborations of Sudoku, logic puzzle, NMCT Sec 2.4, 2.5
  
• 11.9
  Project progress presentation
• 11.16
  NMCT Sec 3.5, 4.1, 4.2, 4.3
  

KR implementations

• ASP
  • SMODELS
    • LPARSE User's Manual
  • DLV
  • CMODELS
  • Potassco (gringo, clasp, and more)
  • f2lp
• Causal Logic and Action Language C+
  • Causal Calculator
• Event Calculus
  • Discrete Event Calculus Reasoner
  • ECASP: Event Calculus as Answer Set Programming
• Frame-based
  • KM:The Knowledge Machine