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The SNePS Approach to Cognitive Robotics

The SNePS Approach to Cognitive Robotics. Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science University at Buffalo shapiro@cse.buffalo.edu. Outline. Introduction Intensional Representation & Propositions SNePS Connectives and Quantifiers

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The SNePS Approach to Cognitive Robotics

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  1. The SNePS Approach to Cognitive Robotics Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science University at Buffalo shapiro@cse.buffalo.edu S.C. Shapiro

  2. Outline • Introduction • Intensional Representation & Propositions • SNePS Connectives and Quantifiers • SNeRE Acting Constructs • Example Plans • Representation and Use of Indexicals • A Personal Sense of Time • Summary S.C. Shapiro

  3. 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

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

  5. 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

  6. 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

  7. 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

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

  9. Cassie, the FEVAHR S.C. Shapiro

  10. FEVAHR/Cassie in the Lab S.C. Shapiro

  11. FEVAHRWorld Simulation S.C. Shapiro

  12. 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

  13. Crystal Space Environment S.C. Shapiro

  14. Outline • Introduction • Intensional Representation & Propositions • SNePS Connectives and Quantifiers • SNeRE Acting Constructs • Example Plans • Representation and Use of Indexicals • A Personal Sense of Time • Summary S.C. Shapiro

  15. Entities, Terms, Symbols, Objects • Cassie’s mental entity: a person named Bill • SNePS term: B5 • Object in world: S.C. Shapiro

  16. Intensional Representation Intensional entities are distinct even if coreferential. “The morning star is the evening star.” “George IV wondered if Scott was the author of Waverly.” S.C. Shapiro

  17. McCarthy’s Telephone Number Problem Mary's telephone number is Mike's telephone number. I understand that Mike's telephone number is Mary's telephone number. Pat knew Mike's telephone number. I understand that Pat knew Mike's telephone number. Pat dialed Mike's telephone number. I understand that Pat dialed Mike's telephone number. S.C. Shapiro

  18. Answering the Telephone Number Problem Did Pat dial Mary's telephone number? Yes, Pat dialed Mary's telephone number. Did Pat know Mary's telephone number? I don't know. S.C. Shapiro

  19. Representing Propositions Propositions must be first-class entities of the domain Represented by terms. S.C. Shapiro

  20. Discussing Propositions 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

  21. Outline • Introduction • Intensional Representation & Propositions • SNePS Connectives and Quantifiers • SNeRE Acting Constructs • Example Plans • Representation and Use of Indexicals • A Personal Sense of Time • Summary S.C. Shapiro

  22. Logic for NLU &Commonsense Reasoning Either Pat is a man or Pat is a woman or Pat is a robot. I understand that Pat is a robot or Pat is a woman or Pat is a man. Pat is a woman. I understand that Pat is a woman. What is Pat? Pat is a woman and Pat is not a robot and Pat is not a man. S.C. Shapiro

  23. Representation in FOPL? Man(Pat)  Woman(Pat)  Robot(Pat) S.C. Shapiro

  24. Representation in FOPL? Man(Pat)  Woman(Pat)  Robot(Pat) but don’t want inclusive or S.C. Shapiro

  25. + + Representation in FOPL? Man(Pat)  Woman(Pat)  Robot(Pat) but don’t want inclusive or Man(Pat) Woman(Pat) Robot(Pat) T T T F T So don’t want exclusive or either S.C. Shapiro

  26. andor andor(i, j){P1, ..., Pn} True iff at least i, and at most j of the Pi are True S.C. Shapiro

  27. thresh thresh(i, j){P1, ..., Pn} True iff either fewer than i, or more than j of the Pi are True Note: thresh(i, j) ~andor(i, j) S.C. Shapiro

  28. or-entailment {P1, ..., Pn} v=> {Q1, ..., Qn} True iff for all i, j Pi Qj S.C. Shapiro

  29. and-entailment {P1, ..., Pn} &=> {Q1, ..., Qn} True iff for all j P1 &…& Pn Qj S.C. Shapiro

  30. Numerical entailment {P1, ..., Pn} i=> {Q1, ..., Qn} True iff for all j andor(i, n){P1, …, Pn }Qj S.C. Shapiro

  31. Universal Quantifier all(ū)({R1(ū),..., Rn(ū)} &=> {C1(ū),..., Cm(ū)}) Every ā that satisfies R1(ū)&…& Rn(ū) also satisfies C1(ū),..., Cm(ū)}) S.C. Shapiro

  32. Numerical Quantifiers nexists(i,j,k)(x) ({P1(x),..., Pn(x)}: {Q(x)})} There are k individuals that satisfy P1(x) ... Pn(x) and, of them, at least i and at most j also satisfy Q(x) S.C. Shapiro

  33. Outline • Introduction • Intensional Representation & Propositions • SNePS Connectives and Quantifiers • SNeRE Acting Constructs • Example Plans • Representation and Use of Indexicals • A Personal Sense of Time • Summary S.C. Shapiro

  34. MENTAL ACTS • Believe(proposition) • Disbelieve(proposition) S.C. Shapiro

  35. Act Selection • Do-One({act1 ... actn}) • Snif(if(condition, act), ... if(condition, act) [else(act)]) S.C. Shapiro

  36. Act Iteration • Do-All({act1 ... actn}) • Sniterate(if(condition, act), ... if(condition, act), [else(act)]) • Snsequence(act1, ..., actn) • Cascade(act1, ..., actn) • P-Do-All({act1, ..., act2}) S.C. Shapiro

  37. Entity Iteration WithSome(var, suchthat, do, [else]) WithAll(var, suchthat, do, [else]) WithSome+(var, suchthat, do, [else]) WithNew(vars, thatare, suchthat, do, [else]) S.C. Shapiro

  38. Proposition/Act Transformers • Achieve(proposition) • ActPlan(act, plan) • GoalPlan(proposition, act) • Precondition(act, proposition) • Effect(act, proposition) • WhenDo(proposition, act) • WheneverDo(proposition, act) • IfDo(proposition, act) S.C. Shapiro

  39. Outline • Introduction • Intensional Representation & Propositions • SNePS Connectives and Quantifiers • SNeRE Acting Constructs • Example Plans • Representation and Use of Indexicals • A Personal Sense of Time • Summary S.C. Shapiro

  40. 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

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

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

  43. A FEVAHR Acting Rule all(p, o) ({Agent(p), Thing(o)} &=> {Precondition(Follow(p, o), Near(p, o)), GoalPlan(Near(p, o), Goto(p, o)), Precondition(Goto(p, o), Lookat(p, o)), ActPlan(Lookat(p, o), Find(p, o))}) Uses a temporal model. S.C. Shapiro

  44. Acting According to the Rule S.C. Shapiro

  45. Acting According to the Rule Follow a red robot. I found a red robot. I am looking at a red robot. S.C. Shapiro

  46. Acting According to the Rule Follow a red robot. I found a red robot. I am looking at a red robot. I went to a red robot. I am near a red robot. I am following a red robot. S.C. Shapiro

  47. A Plan for Blowing up UXOs all(a)(Agent(a) => ActPlan(Blowup(a, UXOs), Act(a, Cascade(SearchforUxo(a), WithSome+(obj, Near(a, obj), WithNew({ch ex}, {Charge(ch), Explosion(ex)}, Possess(a, ch), Cascade(Place(a, ch, obj), Hide(a), Waitfor(a, ex), SearchforUxo(a))), goto(a, SafeZone)))))) S.C. Shapiro

  48. Outline • Introduction • Intensional Representation & Propositions • SNePS Connectives and Quantifiers • SNeRE Acting Constructs • Example Plans • Representation and Use of Indexicals • A Personal Sense of Time • Summary S.C. Shapiro

  49. 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

  50. 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

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