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Knowledge Representation and Reasoning

Knowledge Representation and Reasoning. Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive Science. Introduction. Long-Term Goal. Theory and Implementation of Natural-Language-Competent Computerized Cognitive Agent and Supporting Research in

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Knowledge Representation and Reasoning

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  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

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