Knowledge Representation and Reasoning

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Knowledge Representation and Reasoning
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Overview

• Contact Information
• Grading policy
• Syllabus
• How to fail this class
• What is knowledge?

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General Information

• Instructor Lee McCauley
• 374 Dunn Hall
• 678-2486
• mccauley_at_memphis.edu
• Office Hours Tue. Thur. 100 230

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Evaluation

• Class Participation 20
• Model Presentations 20
• Project Write-up 20
• Project Code/Demo 20
• Homeworks 20

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Syllabus

• Refer to your handouts

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How to fail this class

• Dont show up
• Dont take the project seriously
• Dont do the homework
• Come ill-prepared for presentations

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What is knowledge?

• Discussion

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Historical Background

• Socrates began the art of rhetoric in the fifth
  century BC and died for corrupting the minds of
  Athenian youth
• Plato, his student, created the field of
  epistemology the study of the nature of
  knowledge
• Aristotle, Platos student, (who did not want to
  die) shifted the emphasis from the nature of
  knowledge to the representation of knowledge

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Tentative Definition

• Justified belief that increases an entities
  capacity for effective action (Nonaka 1994, Huber
  1991)

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Data, Information, and Knowledge

• Data
• Raw material/sensation
• Information
• Categorized data
• Data with meaning that may change knowledge
• Knowledge
• Actionable information
• What to do with the information
• Information that can be reasoned to be either
  true or false

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Early AI enthusiasms

• Logic and theorem proving eagerly adopted
• Computational issues forced consideration of how
  to package up knowledge, control inference
• Frame languages
• Special-purpose KR languages
• Formalists versus Hackers

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Form minus content

• Movement in 1980s KR Formal KR
• Reaction to lack of clear semantics
• Identification of formality with precision
• Focus on general logical schemes, not specific
  domains
• Consequences
• Lots of technical progress
• Common perception of sterility in many areas,
  e.g. nonmonotonic logics
• Most exciting KR work didnt appear in KR
  community, e.g., qualitative physics, CYC
  project, ...

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The Representation Resurgence

• Representation Lite hits too many walls
• Web search engines adding more semantics along
  with statistical techniques
• Dramatic success stories in narrow areas
• Scheduling Desert Shield, Detecting money
  laundering, Detecting stolen credit cards
• Steady scientific progress in AI
• KR now embracing content again
• Moores law is making it all practical

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The Future of KR

• Ideas, technologies, and tools now coming
  together
• Clear perception arising of need for common sense
  knowledge bases
• Keeping up with the Web NLP rises again!
• See Semantic Web, DAML projects
• Software that you treat as a collaborator
• Knowledge management
• The infrastructure is being created today
• Those who understand KR will shape what happens

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What this course is about

• You will learn how to represent knowledge very
  precisely
• So precisely that computer programs can use it
• You will learn state of the art representation
  schemes for core kinds of knowledge
• Space, time, quantity, events, causality, common
  sense
• You will learn how to program in a powerful KR
  language - Prolog