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

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/Robot 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; –Maintain a model of itself; –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 Princess from “The Trial, The Trail” A VR drama by Josephine Anstey

16. cse@buffalo S.C. Shapiro Vacuum Cleaner Cassie Using Byron Weber Becker’s Java Karel

17. cse@buffalo S.C. Shapiro Magellan Pro TM Mobile Robot from iRobot

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

19. 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.

20. 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.

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

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

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

24. 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

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

26. 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

27. 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

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

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

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

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

32. cse@buffalo S.C. Shapiro Current Research Issues: Representation & Reasoning with Arbitrary Objects Stuart C. Shapiro in conjunction with Development of SNePS 3

33. 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?

34. 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))

35. cse@buffalo S.C. Shapiro Structural 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)

36. 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)))

37. cse@buffalo S.C. Shapiro Axiomatic Subsumption (Runs in SNePS 3) Animal(any x Mammal(x)) Hairy(any x Mammal(x)) Mammal(any x Dog(x)) Dog(Fido)  Hairy(any x Dog(x)) Hairy(Fido) Animal(Fido)

38. 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, …

39. 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/ –Meets Fridays 9-11, 242 Bell Hall –Join us!