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Knowledge Representation and Reasoning

Explore the theory and implementation of a natural-language-competent computerized cognitive agent, engaging in reasoning, communication, and action. This project focuses on the integration of knowledge representation and reasoning, cognitive robotics, natural language understanding, and generation.

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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 Fellow, AAAI Chair, ACM/SIGART, 1991-1995 President, KR., Inc., 1998-2000 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 NL SNePS Perceptuo-Motor Level Sensory-Actuator Level Vision Sonar Motion Proprioception 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. Interaction with Cassie (Current) Set of Beliefs [SNePS] English (Statement, Question, Command) Reasoning Clarification Dialogue Looking in World GATN Parser (Updated) Set of Beliefs [SNePS] (New Belief) [SNePS] Answer [SNIP] Actions [SNeRE] GATN Generator Reasoning English sentence expressing new belief answering question reporting actions S.C. Shapiro

  10. Example Cassies& Worlds S.C. Shapiro

  11. Cassie, the BlocksWorld Robot S.C. Shapiro

  12. FEVAHR: Award-Winning Embodied Cassie Project S.C. Shapiro

  13. FEVAHRWorld Simulation S.C. Shapiro

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

  15. Crystal Space Environment S.C. Shapiro

  16. UB Virtual Site Museum • The 9th-Century BC Northwest Palace at Nimrud-Iraq is the best preserved and documented of all the Assyrian palaces. • Its audience halls were originally created as the backdrop for differing royal activities. • Completely immersive re-creation of this palace with animated characters and interactive story boards. • T. Kesavadas & S. Paley Modeling of King - Animation in Real time VR S.C. Shapiro

  17. Sample Research IssuesIntensional Entities S.C. Shapiro

  18. Intensional Entities 1 • Rather than represent “objects in the world,” represent mental entities. • Includes Imaginary and Fictional Entities. • Multiple mental entities may correspond to one world object. • Intensional entities may be co-extensional. • But must be kept separate. S.C. Shapiro

  19. Intensional Entities 2 : The morning star is the evening star. I understand that the morning star is the evening star. : The evening star is Venus. I understand that Venus is the evening star. : Clark Kent is Superman. I understand that Superman is Clark Kent. S.C. Shapiro

  20. Intensional Entities 3 : LoisLanesawClarkKent. I understand that Lois Lane saw Clark Kent. : DidLois Lane see Superman? I don't know. : Did Lois Lane see Clark Kent? Yes, Lois Lane saw Clark Kent. Note Open World Assumption. S.C. Shapiro

  21. Intensional Entities 4 : Superman went to the morning star. I understand that Superman went to Venus. : Did Clark Kent go to Venus? Yes, Superman went to Venus. S.C. Shapiro

  22. Intensional Entities 5 : Buck Rogers went to the evening star. I understand that Buck Rogers went to Venus. : Who went to Venus? Buck Rogers went to Venus and Superman went to Venus. S.C. Shapiro

  23. Intensional Entities 6 The evening star The morning star Venus Go to Go to ClarkKent Superman Buck Rogers See Lois Lane S.C. Shapiro

  24. Sample Research IssuesComplex Categories S.C. Shapiro

  25. 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

  26. 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

  27. 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

  28. 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. S.C. Shapiro

  29. PseudoRepresentation of Complex Categories • Isa(B30, CompCat(orange, CompCat(juice, seat))) • Isa(Pat, CompCat(excellent, teacher)) • Isa(Lucy, CompCat(former, teacher)) S.C. Shapiro

  30. Sample Research IssuesPossession S.C. Shapiro

  31. Possession 1 • “One man’s meat is another man’s poison.” S.C. Shapiro

  32. Possession 2 : Richard's meat is Henry's poison. I understand that Henry's poison is Richard's meat. : Edward ate Richard's meat. I understand that Edward ate Richard's meat. : Did Edward eat Henry's poison? Yes, Edward ate Henry's poison. S.C. Shapiro

  33. Possession 3 : Did Edward eat Henry’s meat? I don’t know. : Did Edward eat Richard's poison? I don’t know. Moral: Possession is a three-place relation. S.C. Shapiro

  34. PseudoRepresentation of Possession • Has(Richard, meat, B35) • Has(Henry, poison, B37) • Equiv(B35, B37) S.C. Shapiro

  35. Sample Research IssuesPropositions about Propositions S.C. Shapiro

  36. Propositions about Propositions 1 • Propositions are “first-class” mental entities. • They can be discussed, just like other mental entities. • And must be represented like other mental entities. S.C. Shapiro

  37. Propositions about Propositions 2 : That Bill is sweet is Mary's favorite proposition. I understand that Mary's favorite proposition is that Bill is sweet. : Mike believes Mary's favorite proposition. I understand that Mike believes that Bill is sweet. S.C. Shapiro

  38. Propositions about Propositions 3 : That Mary's favorite proposition is that Bill is sweet is cute. I understand that that Mary's favorite proposition is that Bill is sweet is cute. S.C. Shapiro

  39. Representing Propositions • Representation of Proposition • Not by a Logical Sentence • But by a Functional Term • Denoting a Proposition. S.C. Shapiro

  40. PseudoRepresentation of Propositions about Propositions • Has(Mary, CompCat(favorite, proposition), HasProp(Bill, sweet)) • Believes(Mike, HasProp(Bill, sweet)) • HasProp(Has(Mary, CompCat(favorite, proposition), HasProp(Bill, sweet)), cute) S.C. Shapiro

  41. Sample Research IssuesConditional Plans S.C. Shapiro

  42. Conditional Plans If a block is on a support then a plan to achieve that the support is clear is to pick up the block and then put the block on the table. all(x, y) ({Block(x), Support(y), On(x, y)} &=> {GoalPlan(Clear(y), Snsequence(Pickup(x), Put(x, Table)))}) STRIPS-like representation: No times S.C. Shapiro

  43. Use of Conditional Plan GoalPlan(Clear(B), Snsequence(Pickup(A), Put(A, Table))) Remember (cache) derived propositions. S.C. Shapiro

  44. Use of Conditional Plan GoalPlan(Clear(B), Snsequence(Pickup(A), Put(A, Table)))??? SNeBR to the rescue! S.C. Shapiro

  45. Sample Research IssuesIndexicals S.C. Shapiro

  46. 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

  47. 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

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

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

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

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