1. Knowledge Representation and Reasoning

2. Overview
Contact Information
Grading policy
Syllabus
How to fail this class
What is knowledge?

3. General Information Instructor: Lee McCauley 374 Dunn Hall 678-2486
Office Hours: Tue. & Thur. 1:00 – 2:30

4. Evaluation Class Participation = 20% Model Presentations = 20%
Project Write-up = 20%
Project Code/Demo = 20%
Homeworks = 20%

5. Syllabus
Refer to your handouts

6. How to fail this class Don’t show up Don’t take the project seriously
Don’t do the homework
Come ill-prepared for presentations

7. What is knowledge?
Discussion

8. 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, Plato’s student, (who did not want to die) shifted the emphasis from the nature of knowledge to the representation of knowledge

9. Tentative Definition
Justified belief that increases an entities capacity for effective action (Nonaka 1994, Huber 1991)

10. Data, Information, and Knowledge
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

11. 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

12. Form minus content Movement in 1980s: KR = Formal KR Consequences
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 didn’t appear in KR community, e.g., qualitative physics, CYC project, ...

13. 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
Moore’s law is making it all practical

14. 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

15. 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