1. cse@buffalo S.C. Shapiro An Introduction to SNePS 3 Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science State University of New York at Buffalo shapiro@cse.buffalo.edu

2. cse@buffalo S.C. Shapiro Outline Setting Basic SNePS Principles Examples 4 Kinds of Inference Summary

3. cse@buffalo S.C. Shapiro Parentage of SNePS 3 SNePS 2.5 ANALOG –Structured (Conceptually Complete) Variables Currently being implemented –in CLOS and/or Java.

4. cse@buffalo S.C. Shapiro SNePS KRR Style Network-based Logic-based Intended as the LOT of a NL-competent cognitive agent.

5. cse@buffalo S.C. Shapiro Outline Setting Basic SNePS Principles Examples 4 Kinds of Inference Summary

6. cse@buffalo S.C. Shapiro Basic SNePS Principles A Summary of Syntax and Semantics Propositional Semantic Network Term Logic Intensional Representation Uniqueness Principle Paraconsistent Logic.

7. cse@buffalo S.C. Shapiro Propositional Semantic Network The only well-formed SNePS expressions are nodes. –Arcs do not have semantics Do not have assertional import

8. cse@buffalo S.C. Shapiro Term Logic Every well-formed SNePS expression is a term. –Even propositions are denoted by terms. –Propositions can be arguments without leaving first- order logic.

9. cse@buffalo S.C. Shapiro Intensional Representation SNePS terms represent (denote) intensional (mental) entities. –Cognitively distinct entities denoted by distinct terms Even if co-extensional –Every term denotes a mental entity. No term for purely technical reasons

10. cse@buffalo S.C. Shapiro Uniqueness Principle No two SNePS terms denote the same entity. –Syntactically distinct terms are semantically distinct. –Full structure sharing.

11. cse@buffalo S.C. Shapiro Paraconsistent Logic A contradiction does not imply anything whatsoever. –A contradiction in one subdomain does not corrupt another.

12. cse@buffalo S.C. Shapiro Outline Setting Basic SNePS Principles Examples 4 Kinds of Inference Summary

13. cse@buffalo S.C. Shapiro Example: Term Logic & Conceptual Relations

14. cse@buffalo S.C. Shapiro Example SNePS Ontology

15. cse@buffalo S.C. Shapiro Example SNePS Ontology

16. cse@buffalo S.C. Shapiro Example SNePS Ontology

17. cse@buffalo S.C. Shapiro Example SNePS Ontology

18. cse@buffalo S.C. Shapiro Example SNePS Ontology

19. cse@buffalo S.C. Shapiro Example SNePS Ontology

20. cse@buffalo S.C. Shapiro Cassie talks to Stu

21. cse@buffalo S.C. Shapiro Outline Setting Basic SNePS Principles Examples 4 Kinds of Inference Summary

22. cse@buffalo S.C. Shapiro Wire-Based Inference

23. cse@buffalo S.C. Shapiro Wire-Based Inference

24. cse@buffalo S.C. Shapiro Path-Based Inference

25. cse@buffalo S.C. Shapiro Path-Based Inference member class

26. cse@buffalo S.C. Shapiro Path-Based Inference

27. cse@buffalo S.C. Shapiro Node-Based Inference If B1 is a talking robot, then B1 is intelligent.

28. cse@buffalo S.C. Shapiro Node-Based Inference

29. cse@buffalo S.C. Shapiro Node-Based Inference

30. cse@buffalo S.C. Shapiro Node-Based Inference

31. cse@buffalo S.C. Shapiro SNePS 2.5 Generic Version

32. cse@buffalo S.C. Shapiro Subsumption Inference

33. cse@buffalo S.C. Shapiro Outline Setting Basic SNePS Principles Examples 4 Kinds of Inference Summary

34. cse@buffalo S.C. Shapiro Summary SNePS: a Logic- and Network-Based KRR with its own Syntax, Semantics, Proof Theory SNePS 3 has 4 kinds of inference –Wire-based –Path-based –Node-based –Subsumption SNePS 3 is currently being implemented.

35. cse@buffalo S.C. Shapiro SNeRG Home Page http://www.cse.buffalo.edu/sneps/

36. cse@buffalo S.C. Shapiro Wire-Based Inference Assume (define-relation :name“member” :typeentity :adjustreduce :limit1)

37. cse@buffalo S.C. Shapiro Path-Based Inference Assume (define-path class (compose class (kstar (compose subclass- ! superclass)))

38. cse@buffalo S.C. Shapiro Node-Based Inference E.g. Using and-entailment {P 1, …, P n } &=> {Q 1, …, Q m }