Advanced Knowledge Representation and Reasoning in Cognitive Agents

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

2. Introduction S.C. Shapiro

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

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

5. 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. S.C. Shapiro

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

7. GLAIR Architecture Grounded Layered Architecture with Integrated Reasoning Knowledge Level SNePS Perceptuo-Motor Level NL Sensory-Actuator Level Vision Sonar Proprioception Motion S.C. Shapiro

8. 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. S.C. Shapiro

9. Example Cassies& Worlds S.C. Shapiro

10. BlocksWorld S.C. Shapiro

11. FEVAHR S.C. Shapiro

12. FEVAHRWorld Simulation S.C. Shapiro

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

14. Crystal Space Environment S.C. Shapiro

15. Sample Research Issues:Complex Categories S.C. Shapiro

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

17. Complex Categories 2 : 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. S.C. Shapiro

18. Complex Categories 3 : 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. S.C. Shapiro

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

20. Sample Research Issues:Indexicals S.C. Shapiro

21. 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, *YOU, *NOW> • *I: SNePS term representing Cassie • *YOU: person Cassie is talking with • *NOW: current time. S.C. Shapiro

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

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

24. Use of Indexicals 1 Come here. S.C. Shapiro

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

26. Use of Indexicals 3 Whoam I? Your name is ‘Stu’ and you are a person. Whohaveyoutalkedto? I am talking to you. TalktoBill. I am talking to you, Bill. Comehere. S.C. Shapiro

27. Use of Indexicals 4 Comehere. I found you. I am looking at you. S.C. Shapiro

28. Use of Indexicals 5 Comehere. I found you. I am looking at you. I came to you. I am near you. S.C. Shapiro

29. Use of Indexicals 6 WhoamI? Your name is ‘Bill’ and you are a person. Whoareyou? I am the FEVAHR and my name is ‘Cassie’. Whohaveyoutalkedto? I talked to Stu and I am talking to you. S.C. Shapiro

30. Current Research Issues: Distinguishing Perceptually Indistinguishable ObjectsPh.D. Dissertation, John F. Santore S.C. Shapiro

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

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

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

34. Current Research Issues: Belief Revisionin aDeductively Open Belief SpacePh.D. Dissertation, Frances L. Johnson S.C. Shapiro

35. 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 S.C. Shapiro

36. 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 S.C. Shapiro

37. 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 S.C. Shapiro

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

39. Next Steps • Implement reconsideration • Develop benchmarks for implemented krr systems. S.C. Shapiro

40. Current Research Issues: Default ReasoningbyPreferential Ordering of BeliefsM.S. Thesis, Bharat Bhushan S.C. Shapiro

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

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

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

44. Next Steps • Finish theory and implementation. S.C. Shapiro

45. Current Research Issues: Representation & Reasoningwith Arbitrary ObjectsStuart C. Shapiro S.C. Shapiro

46. 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? S.C. Shapiro

47. 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)) S.C. Shapiro

48. 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) S.C. Shapiro

49. 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))) S.C. Shapiro

50. 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, … S.C. Shapiro