1. cse@buffalo S.C. Shapiro Knowledge Representation and Reasoning Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive Science

2. cse@buffalo S.C. Shapiro Introduction

3. cse@buffalo S.C. Shapiro Long-Term Goal Theory and Implementation of Natural-Language-Competent Computerized Cognitive Agent and Supporting Research in Artificial Intelligence Cognitive Science Computational Linguistics.

4. cse@buffalo S.C. Shapiro Research Areas Knowledge Representation and Reasoning Cognitive Robotics Natural-Language Understanding Natural-Language Generation.

5. cse@buffalo S.C. Shapiro Goal A computational cognitive agent that can: –Understand and communicate in English; –Discuss specific, generic, and “rule-like” information; –Reason; –Discuss acts and plans; –Sense; –Act; –Remember and report what it has sensed and done.

6. cse@buffalo S.C. Shapiro Cassie A computational cognitive agent –Embodied in hardware –or Software-Simulated –Based on SNePS and GLAIR.

7. cse@buffalo S.C. Shapiro GLAIR Architecture Knowledge Level Perceptuo-Motor Level Sensory-Actuator Level NL Vision Sonar MotionProprioception Grounded Layered Architecture with Integrated Reasoning SNePS

8. cse@buffalo S.C. Shapiro SNePS Knowledge Representation and Reasoning –Propositions as Terms SNIP: SNePS Inference Package –Specialized connectives and quantifiers SNeBR: SNePS Belief Revision SNeRE: SNePS Rational Engine Interface Languages –SNePSUL: Lisp-Like –SNePSLOG: Logic-Like –GATN for Fragments of English.

9. cse@buffalo S.C. Shapiro Example Cassies & Worlds

10. cse@buffalo S.C. Shapiro BlocksWorld

11. cse@buffalo S.C. Shapiro FEVAHR

12. cse@buffalo S.C. Shapiro FEVAHRWorld Simulation

13. cse@buffalo S.C. Shapiro UXO Remediation Cassie Corner flag NonUXO object Corner flag UXO Battery meter Corner flag Drop-off zone Field Safe zone Recharging Station

14. cse@buffalo S.C. Shapiro Crystal Space Environment

15. cse@buffalo S.C. Shapiro Sample Research Issues: Complex Categories

16. cse@buffalo S.C. Shapiro Complex Categories 1 Noun Phrases: {N | Adj}* N Understanding of the modification must be left to reasoning. Example: orange juice seat Representation must be left vague.

17. cse@buffalo S.C. Shapiro : Kevin went to the orange juice seat. I understand that Kevin went to the orange juice seat. : Did Kevin go to a seat? Yes, Kevin went to the orange juice seat. Complex Categories 2

18. cse@buffalo S.C. Shapiro : Pat is an excellent teacher. I understand that Pat is an excellent teacher. : Is Pat a teacher? Yes, Pat is a teacher. : Lucy is a former teacher. I understand that Lucy is a former teacher. Complex Categories 3

19. cse@buffalo S.C. Shapiro : `former' is a negative adjective. I understand that `former' is a negative adjective. : Is Lucy a teacher? No, Lucy is not a teacher. Complex Categories 4 Also note representation and use of knowledge about words.

20. cse@buffalo S.C. Shapiro Sample Research Issues: Indexicals

21. cse@buffalo S.C. Shapiro Representation and Use of Indexicals Words whose meanings are determined by occasion of use E.g. I, you, now, then, here, there Deictic Center *I : SNePS term representing Cassie *YOU: person Cassie is talking with *NOW: current time.

22. cse@buffalo S.C. Shapiro Analysis of Indexicals (in input) First person pronouns: *YOU Second person pronouns: *I “here”: location of *YOU Present/Past relative to *NOW.

23. cse@buffalo S.C. Shapiro Generation of Indexicals *I : First person pronouns *YOU : Second person pronouns *NOW : used to determine tense and aspect.

24. cse@buffalo S.C. Shapiro Come here. Use of Indexicals 1

25. cse@buffalo S.C. Shapiro Come here. I came to you, Stu. I am near you. Use of Indexicals 2

26. cse@buffalo S.C. Shapiro Who am I? Your name is ‘Stu’ and you are a person. Who have you talked to? I am talking to you. Talk to Bill. I am talking to you, Bill. Come here. Use of Indexicals 3

27. cse@buffalo S.C. Shapiro Come here. I found you. I am looking at you. Use of Indexicals 4

28. cse@buffalo S.C. Shapiro Come here. I came to you. I am near you. I found you. I am looking at you. Use of Indexicals 5

29. cse@buffalo S.C. Shapiro Who am I? I talked to Stu and I am talking to you. Your name is ‘Bill’ and you are a person. Who are you? I am the FEVAHR and my name is ‘Cassie’. Who have you talked to? Use of Indexicals 6

30. cse@buffalo S.C. Shapiro Current Research Issues: Distinguishing Perceptually Indistinguishable Objects Ph.D. Dissertation, John F. Santore

31. cse@buffalo S.C. Shapiro Some robots in a suite of rooms.

32. cse@buffalo S.C. Shapiro Are these the same two robots? Why do you think so/not?

33. cse@buffalo S.C. Shapiro Next Steps How do people do this? –Currently doing protocol experiments Getting Cassie to do it.

34. cse@buffalo S.C. Shapiro Current Research Issues: Belief Revision in a Deductively Open Belief Space Ph.D. Dissertation, Frances L. Johnson

35. cse@buffalo S.C. Shapiro Belief Revision in a Deductively Open Belief Space Beliefs in a knowledge base must be able to be changed (belief revision) –Add & remove beliefs –Detect and correct errors/conflicts/inconsistencies BUT … –Guaranteeing consistency is an ideal concept –Real world systems are not ideal

36. cse@buffalo S.C. Shapiro Belief Revision in a DOBS Ideal Theories vs. Real World Ideal Belief Revision theories assume: –No reasoning limits (time or storage) All derivable beliefs are acquirable (deductive closure) –All belief credibilities are known and fixed Real world –Reasoning takes time, storage space is finite Some implicit beliefs might be currently inaccessible –Source/belief credibilities can change

37. cse@buffalo S.C. Shapiro Belief Revision in a DOBS A Real World KR System Must recognize its limitations –Some knowledge remains implicit –Inconsistencies might be missed –A source turns out to be unreliable –Revision choices might be poor in hindsight After further deduction or knowledge acquisition Must repair itself –Catch and correct poor revision choices

38. cse@buffalo S.C. Shapiro Belief Revision in a DOBS Theory Example – Reconsideration College A is better than College B. (Source: Ranking 1) College B is better than College A. (Source: Ranking 2) Ranking 1 is more credible that Ranking 2. Ranking 1 was flawed, so Ranking 2 is more credible than Ranking 1. Need to reconsider! Ranking 1 is more credible that Ranking 2. College B is better than College A. (Source: Ranking 2)

39. cse@buffalo S.C. Shapiro Next Steps Implement reconsideration Develop benchmarks for implemented krr systems.

40. cse@buffalo S.C. Shapiro Current Research Issues: Default Reasoning by Preferential Ordering of Beliefs M.S. Thesis, Bharat Bhushan

41. cse@buffalo S.C. Shapiro Small Knowledge Base Birds have wings. Birds fly. Penguins are birds. Penguins don’t fly.

42. cse@buffalo S.C. Shapiro KB Using Default Logic  x(Bird(x)  Has(x, wings))  x(Penguin(x)  Bird(x))  x(Penguin(x)   Flies(x)) Bird(x): Flies(x) Flies(x)

43. cse@buffalo S.C. Shapiro KB Using Preferential Ordering  x(Bird(x)  Has(x, wings))  x(Penguin(x)  Bird(x))  x(Penguin(x)   Flies(x))  x(Bird(x)  Flies(x)) Precludes(  x(Penguin(x)   Flies(x)),  x(Bird(x)  Flies(x)))

44. cse@buffalo S.C. Shapiro Next Steps Finish theory and implementation.

45. cse@buffalo S.C. Shapiro Current Research Issues: Representation & Reasoning with Arbitrary Objects Stuart C. Shapiro

46. cse@buffalo S.C. Shapiro Classical Representation Clyde is gray. –Gray(Clyde) All elephants are gray. –  x(Elephant(x)  Gray(x)) Some elephants are albino. –  x(Elephant(x) & Albino(x)) Why the difference?

47. cse@buffalo S.C. Shapiro Representation Using Arbitrary & Indefinite Objects Clyde is gray. –Gray(Clyde) Elephants are gray. –Gray(any x Elephant(x)) Some elephants are albino. –Albino(some x Elephant(x))

48. cse@buffalo S.C. Shapiro Subsumption Among Arbitrary & Indefinite Objects (any x Elephant(x)) (any x Albino(x) & Elephant(x)) (some x Albino(x) & Elephant(x)) (some x Elephant(x)) If x subsumes y, then P(x)  P(y)

49. cse@buffalo S.C. Shapiro Example (Runs in SNePS 3) Hungry(any x Elephant(x) & Eats(x, any y Tall(y) & Grass(y) & On(y, Savanna)))  Hungry(any u Albino(u) & Elephant(u) & Eats(u, any v Grass(v) & On(v, Savanna)))

50. cse@buffalo S.C. Shapiro Next Steps Finish theory and implementation of arbitrary and indefinite objects. Extend to other generalized quantifiers –Such as most, many, few, no, both, 3 of, …

51. cse@buffalo S.C. Shapiro For More Information Shapiro: http://www.cse.buffalo.edu/~shapiro/ http://www.cse.buffalo.edu/~shapiro/ SNePS Research Group: http://www.cse.buffalo.edu/sneps/ http://www.cse.buffalo.edu/sneps/