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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 Faculty Member, Interdisciplinary MS in Computational Linguistics. Introduction. Long-Term Goal. Theory and Implementation of

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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 Faculty Member, Interdisciplinary MS in Computational Linguistics S.C. Shapiro

  2. Introduction S.C. Shapiro

  3. Long-Term Goal • Theory and Implementation of Natural-Language-Competent Computerized Cognitive Agent/Robot • 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; • Maintain a model of itself; • 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. Princess from “The Trial, The Trail” A VR drama by Josephine Anstey S.C. Shapiro

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

  17. Magellan ProTM Mobile RobotfromiRobot S.C. Shapiro

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

  19. 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

  20. 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

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

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

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

  24. 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

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

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

  27. 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

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

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

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

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

  32. Current Research Issues: Representation & Reasoningwith Arbitrary ObjectsStuart C. Shapiroin conjunction with Development of SNePS 3 S.C. Shapiro

  33. 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

  34. 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

  35. 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) S.C. Shapiro

  36. 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

  37. 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) S.C. Shapiro

  38. 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

  39. For More Information • Shapiro:http://www.cse.buffalo.edu/~shapiro/ • SNePS Research Group:http://www.cse.buffalo.edu/sneps/ • Meets Fridays 9-11, 242 Bell Hall • Join us! S.C. Shapiro

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