1. Lecture 8-2CS250: Intro to AI/Lisp What do you mean, “What do I mean?” Lecture 8-2 November 18 th, 1999 CS250

2. Lecture 8-2CS250: Intro to AI/Lisp Last Time First-order logic –Frame problem –Successor state axioms Situation calculus

3. Lecture 8-2CS250: Intro to AI/Lisp The British Nationality Act What happened? What’s the point? What are the problems that arise? What do the solutions look like?

4. Lecture 8-2CS250: Intro to AI/Lisp Why do we need shared meanings?

5. Lecture 8-2CS250: Intro to AI/Lisp In agent based systems, such as proposed for next generation electronic commerce, the adoption of a shared ontology allows commerce agents to simultaneously:  interoperate without misunderstanding,  retain a high degree of autonomy, flexibility and agility. Commerce agents can therefore be highly adaptable yet are able to meaningfully communicate domain-specific knowledge. They do this by using only the basic terms and relationships defined in the ontology. It is the precise definitions of the basic terms that allows those terms to be combined to form meaningful higher level knowledge Ontology.org

6. Lecture 8-2CS250: Intro to AI/Lisp Ontolingua Stanford ontology server –Suite of ontology authoring tools –Library of modular reusable ontologies

7. Lecture 8-2CS250: Intro to AI/Lisp How do we build an ontology? Knowledge engineering Knowledge acquisition Ontological engineering

8. Lecture 8-2CS250: Intro to AI/Lisp Representing U of C What kinds of questions might we want to ask of our knowledge base?

9. Lecture 8-2CS250: Intro to AI/Lisp Steps in Building Decide what to talk about Decide on a vocabulary Encode general rules Encode an instance Pose queries

10. Lecture 8-2CS250: Intro to AI/Lisp What do we get from logic? Logics consist of: –Syntax –Semantics –Proof theory Expressive, but doesn’t say what to express

11. Lecture 8-2CS250: Intro to AI/Lisp A Few Terms Knowledge engineering - Art & science of transforming worldly knowledge into computer reasonable form Knowledge acquisition - Squeezing knowledge from the heads of experts

12. Lecture 8-2CS250: Intro to AI/Lisp Declarative Approach Rides Again Write down what you know, and let the system figure out the rest Separate inferencing from representation –Design an inferencing engine that works with many representations –Free to focus on the best representation

13. Lecture 8-2CS250: Intro to AI/Lisp Good Qualities for a Knowledge Base Clarity Coherence Extensibility Avoid favoring encodings Minimal ontological commitment From “Toward Principles for the Design of Ontologies Used for Knowledge Sharing”“Toward Principles for the Design of Ontologies Used for Knowledge Sharing”

14. Lecture 8-2CS250: Intro to AI/Lisp KE Questions For every sentence added to the knowledge base: –Why is this true? Can its truth be decomposed? –Is it widely applicable? Can I broaden this observation? –Do I need a predicate to denote this class of objects? How does the class relate to other classes? Subclasses? Other class properties?

15. Lecture 8-2CS250: Intro to AI/Lisp KE Strategy  Decide what to talk about –What to focus on, what to ignore  Vocabulary of predicates, functions & constants  Encode general domain knowledge  Encode a specific problem instance  Sit back and ask questions

16. Lecture 8-2CS250: Intro to AI/Lisp 1-Bit Adder 1 2 3 2 1

17. Lecture 8-2CS250: Intro to AI/Lisp What are We Talking About Some concepts we’ll need –Wires as connectors –Gates (AND, OR, XOR & NOT) –Inputs –Outputs What don’t we need? Latency, layout, CMOS, time

18. Lecture 8-2CS250: Intro to AI/Lisp Representing Stuff Distinguish gates from one another –Constants Gate types –Type functions > Type(X1) = XOR Terminals –Output terminal function: Out(1, X1) Connectivity

19. Lecture 8-2CS250: Intro to AI/Lisp Encode General Rules If two terminals are connected, they have the same signal The signal at every terminal is either on or off (but not both) An XOR gate is on iff its inputs are different  t1,t2 Connected(t1,t2)  Signal(t1)=Signal(t2)  t Signal(t)=On  Signal(t)=Off On  Off  g Type(g)=XOR  Signal(Out(1,g)=On  Signal(In(1,g))  Signal(In(2,g))

20. Lecture 8-2CS250: Intro to AI/Lisp Encode Specific Instance Encode the circuit –Gate info –Connections among gates

21. Lecture 8-2CS250: Intro to AI/Lisp Ask the $64,000 Question When will the first output of C1 be off and the second output of C1 to be on? Is the circuit correct? –What are the possible sets of values of all the terminals for the adder circuit?  i1,i2,o1,o2 Signal(In(1,C1))=i1  Signal(In(2,C1))=i2  Signal(In(3,C1))=i3  Signal(Out(1,C1))=o1  Signal(Out(2,C1))=o2

22. Lecture 8-2CS250: Intro to AI/Lisp Other KR’s Case-based reasoning Bayesian networks Neural networks

23. Lecture 8-2CS250: Intro to AI/Lisp General Ontologies Categories Measures Composite Objects Time, Space and Change Events and Processes Physical Objects Substances Mental Objects and Beliefs

24. Lecture 8-2CS250: Intro to AI/Lisp Categories Reification –How many people live on Earth? Inheritance Creating taxonomies –Kentucky Fried Chicken –Dewey decimal –LoC –MeSh

25. Lecture 8-2CS250: Intro to AI/Lisp Measures Examples: Height, mass, cost Measure = Units function + a Number

26. Lecture 8-2CS250: Intro to AI/Lisp Composite Objects Not inheritance –Difference between subclass and member Schema Script