Knowledge Representation and Reasoning

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

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

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

Syllabus Refer to your handouts

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

What is knowledge? Discussion

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

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

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

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

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, ...

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

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

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