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The New Age of Commonsense Reasoning

The New Age of Commonsense Reasoning. Henry Kautz University of Rochester. The Idea of Commonsense Reasoning. Programs with Common Sense (McCarthy 1959): Use declarative representations of knowledge and general reasoning methods to Understand the physical world

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The New Age of Commonsense Reasoning

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  1. The New Age of Commonsense Reasoning Henry Kautz University of Rochester

  2. The Idea of Commonsense Reasoning • Programs with Common Sense (McCarthy 1959): • Use declarative representations of knowledge and general reasoning methods to • Understand the physical world • Understand human behavior • Motivation • Tasks: “advice taker”, story understanding • Step towards true AI

  3. Commonsense Reasoning, circa 1985 • Declarative and general • Missing: grounding, learning, probabilities, scalable inference

  4. Commonsense Reasoning, circa 2008

  5. Commonsense Reasoning, circa 2008 • Methods for grounding theories in sensor data • Graphical models for learning & probabilistic inference • Practical (low-exponent) reasoning algorithms

  6. New Applications • Caregiving systems • AI-based assistive technology • Assisted cognition • Automated planning • Supra-expert level • Verification • Natural languageprocessing . . .

  7. This Talk • Monitoring activities of daily living • Learning and guiding users through transportation plans • Planning as satisfiability

  8. Object-Based Activity Recognition • Activities of daily living involve the manipulation of many physical objects • Kitchen: stove, pans, dishes, … • Bathroom: toothbrush, shampoo, towel, … • Bedroom: linen, dresser, clock, clothing, … • We can recognize activities from a time-sequence of object touches (Philipose et al. 2004)

  9. Applications • ADL (Activity of Daily Living) monitoring for the disabled and/or elderly • Changes in routine often precursor to illness, accidents • Human monitoring intrusive & inaccurate • Basis for ADL prompting / reminding Image Courtesy Intel Research

  10. Sensing Object Manipulation • RFID: Radio-frequency identification tags • Small • Durable • Cheap • Easy to tag objects • Near future: use products’ own tags • IRS bracelet reader • 13.56MHz reader, radio, power supply, antenna • 12 inch range, 12-150 hour lifetime

  11. Experiment: Morning Activities • 10 days of data from the morning routine in an experimenter’s home (Patterson, Fox, & Kautz 2005) • 11 activities, 61 tagged objects • Often interleaved and interrupted • Many shared objects

  12. Most Likely Single-State HMM • Each activity modeled as an independent single-state Hidden Markov Model • Captures relationship between activities and objects • Compute probabilities from labeled training data • Compute most likely model at each time point • 68% accuracy

  13. HMM with One State per Activity • Single HMM where each activity is a state • Captures probability of transitioning between activities • Compute most state sequence • 88% accuracy

  14. HMM with Multiple States per Activity • HMM with (# objects) states per activity • Idea: model internal structure of activities as well as transitions between activies • 87% accuracy • Performance degrades

  15. Model Complexity Tradeoff • Too few parameters: cannot fit data • Too many parameters: poor generalization • Let a = # different activities b = # objects per activity • Number of parameters: • HMM with 1 state per activity: O(a2 + ab) • Too simple • HMM with b states per activity: O(a2b2) • Too complex

  16. Interleaved HMM • Idea: explicitly distinguish progress within an activity from switching between activities (Bai & Kautz 2008) • Each activity modeled by an individual HMM • User modeled by a set of activities • One activity is active, others are suspended • When an observation is made, credit it to the current activity, or switch to a different activity Frontier

  17. Interleaved HMM Filtering

  18. Results

  19. Interleaved HMM Confusion Matrix

  20. From Activity Recognition to ADL Prompting • Collaboration with Attention Control Systems to integrate activity monitoring into PEAT (Planning and Execution Assistant and Trainer) • DARPA Small Business Innovative Research

  21. This Talk • Monitoring activities of daily living • Learning and guiding users through transportation plans • Planning as satisfiability

  22. Motivation: Community Access for Persons with Cognitive Disabilities

  23. Problems • Using public transit cognitively challenging • Learning bus routes and transfers • Recovering from mistakes • Point to point shuttle service impractical • Slow • Expensive • Current GPS units hard to use • Require extensive user input • No help with transfers, timing

  24. Solution: Opportunity Knocks • User carries GPS-enabled cell phone • System infers transportation use • System learns model of typical user behavior • Novel events = possible user errors

  25. Modeling Challenge • Create a model of a user’s • Significant places • Modes of transportation • Transportation plans • Approach: • Encode a commonsense theory of transportation use as a Dynamic Bayesian Network (DBN) • Learn the parameters of the DBN from raw GPS data using Expectation-Maximization

  26. Transportation Plans Home A B Work • Goal: ultimate destination • Home, work, friends, stores, doctors, … • Places: goals or intermediate waypoints • Trip segments: <start, end, mode> • Plan = sequence of trip segments • Home to Bus stop A on Foot • Bus stop A to Bus stop B on Bus • Bus stop B to workplace on Foot

  27. Detecting User Errors • Learned model represents typical correct behavior • Model is a poor fit to user errors • We can use this fact to detect errors! • Novelty • Normal: model functions as before • Abnormal: switch in prior (untrained) parameters for mode and edge transition

  28. Dynamic Bayesian Net ck-1 ck Novelty gk-1 gk Goal tk-1 tk Trip segment mk-1 mk Transportation mode xk-1 xk Edge, velocity, position qk-1 qk GPS / pathway association zk-1 zk GPS reading Time k Time k-1

  29. Predict Goal and Path

  30. Detecting Novel Events

  31. Further Work • Wizard of Oz study of direction-giving strategies (Liu et al. 2006) • Images, text, audio • Directions, landmarks • Subjects with cognitive disabilities • Current effort: generating prompts using a Markov Decision Process model • Account for probability that prompt will be understood • Adapt to user

  32. This Talk • Monitoring activities of daily living • Learning and guiding users through transportation plans • Planning as satisfiability

  33. Reasoning about the Physical World • The planning problem: • Given representations of • An initial state of affairs • A desired (goal) state of affairs • A set of actions defined by preconditions and effects • Find a (shortest) sequence of actions that transforms the initial state into a goal state • All useful formalizations of planning are NP-complete or worse • In practice: desire low-exponential algorithms

  34. Planning as Inference • Advice taker (McCarthy 1959) • Proof checker • Planning as first-order theorem proving (Green 1969) • Computationally infeasible • STRIPS (Fikes & Nilsson 1971) • Limited theorem proving + state-space search • Very hard • UCPOP (Weld 1992) • Specialized theorem prover for partial-order planning • Starting to scale up • SATPLAN (Kautz & Selman 1996, 1999, 2000, 2006, 2007) • Planning as general propositional reasoning • Solves many hard problems (60+ steps) optimally

  35. Planning as Satisfiability • Time = bounded sequence of integers • Translate planning problem to a Boolean formula • Find a satisfying truth assignment using a SAT engine • Translate truth assignment to a plan

  36. Benchmark Results • International Planning Competitions (2004 & 2006) • 1st place for deterministic optimal planning • Reason: Progress in scaling SAT solvers • Handful of key ideas

  37. Key Improvements • Stochastic local search • Walksat (Selman, Kautz, & Cohen 1994) • Combines gradient descent with random walk

  38. Key Improvements • Pruning methods for backtrack search • Clause learning (Bayardo & Schrag 1997) • Improves power of underlying proof system (Beame, Kautz, & Sabharwal 2003) Clauselearning Regular RES DPLL

  39. Key Improvements • Restart strategies (Gomes, Selman, & Kautz 1999; Ruan, Kautz, & Horvitz 2004) • Reasoning engines often exhibit heavy-tailed run time distributions (over initial random seeds used for tie-breaking branching heuristics) • Frequent restarts eliminate heavy tails, can provide dramatic speedup

  40. Supra-Human Planning ; Time 177.13 ; ParsingTime 0.00 ; MakeSpan 8 0: (TURN_TO SATELLITE0 STAR2 STAR8) [1] 0: (TURN_TO SATELLITE1 STAR0 GROUNDSTATION3) [1] 0: (TURN_TO SATELLITE2 STAR2 STAR4) [1] 0: (TURN_TO SATELLITE3 STAR2 PHENOMENON9) [1] 0: (TURN_TO SATELLITE4 STAR2 PHENOMENON9) [1] 0: (SWITCH_ON INSTRUMENT0 SATELLITE0) [1] 0: (SWITCH_ON INSTRUMENT3 SATELLITE1) [1] 0: (SWITCH_ON INSTRUMENT4 SATELLITE2) [1] 0: (SWITCH_ON INSTRUMENT7 SATELLITE3) [1] 0: (SWITCH_ON INSTRUMENT8 SATELLITE4) [1] 1: (CALIBRATE SATELLITE1 INSTRUMENT3 STAR0) [1] 1: (CALIBRATE SATELLITE2 INSTRUMENT4 STAR2) [1] 1: (CALIBRATE SATELLITE3 INSTRUMENT7 STAR2) [1] 1: (CALIBRATE SATELLITE4 INSTRUMENT8 STAR2) [1] 1: (TURN_TO SATELLITE0 STAR0 STAR2) [1] 2: (TURN_TO SATELLITE1 STAR10 STAR0) [1] 2: (TURN_TO SATELLITE2 PHENOMENON7 STAR2) [1] 2: (TURN_TO SATELLITE3 STAR5 STAR2) [1] 2: (TURN_TO SATELLITE4 PLANET6 STAR2) [1] 2: (CALIBRATE SATELLITE0 INSTRUMENT0 STAR0) [1] 3: (TURN_TO SATELLITE0 PLANET6 STAR0) [1] 3: (TAKE_IMAGE SATELLITE1 STAR10 INSTRUMENT3 THERMOGRAPH2) [1] 3: (TAKE_IMAGE SATELLITE2 PHENOMENON7 INSTRUMENT4 INFRARED1) [1] 3: (TAKE_IMAGE SATELLITE2 PHENOMENON7 INSTRUMENT4 IMAGE3) [1] 3: (TAKE_IMAGE SATELLITE2 PHENOMENON7 INSTRUMENT4 INFRARED0) [1] 3: (TAKE_IMAGE SATELLITE3 STAR5 INSTRUMENT7 IMAGE3) [1] 3: (TAKE_IMAGE SATELLITE4 PLANET6 INSTRUMENT8 INFRARED0) [1] 3: (TAKE_IMAGE SATELLITE4 PLANET6 INSTRUMENT8 INFRARED1) [1] 4: (TURN_TO SATELLITE2 PHENOMENON15 PHENOMENON7) [1] 4: (TURN_TO SATELLITE3 STAR8 STAR5) [1] 4: (TURN_TO SATELLITE1 STAR1 STAR10) [1] 4: (TURN_TO SATELLITE4 GROUNDSTATION3 PLANET6) [1] 5: (TURN_TO SATELLITE0 PLANET13 PLANET6) [1] 5: (TURN_TO SATELLITE1 PLANET14 STAR1) [1] 5: (TURN_TO SATELLITE4 PHENOMENON7 GROUNDSTATION3) [1] 5: (TAKE_IMAGE SATELLITE2 PHENOMENON15 INSTRUMENT4 INFRARED1) [1] 5: (TAKE_IMAGE SATELLITE2 PHENOMENON15 INSTRUMENT4 INFRARED0) [1] 5: (TAKE_IMAGE SATELLITE3 STAR8 INSTRUMENT7 IMAGE3) [1] 6: (TURN_TO SATELLITE2 STAR17 PHENOMENON15) [1] 6: (TURN_TO SATELLITE3 PLANET16 STAR8) [1] 6: (TURN_TO SATELLITE4 STAR11 PHENOMENON7) [1] 6: (TAKE_IMAGE SATELLITE0 PLANET13 INSTRUMENT0 SPECTROGRAPH4) [1] 6: (TAKE_IMAGE SATELLITE1 PLANET14 INSTRUMENT3 THERMOGRAPH2) [1] 7: (TAKE_IMAGE SATELLITE2 STAR17 INSTRUMENT4 INFRARED0) [1] 7: (TAKE_IMAGE SATELLITE3 PLANET16 INSTRUMENT7 IMAGE3) [1] 7: (TAKE_IMAGE SATELLITE4 STAR11 INSTRUMENT8 INFRARED1) [1] 7: (TURN_TO SATELLITE0 PHENOMENON9 PLANET13) [1] 7: (TURN_TO SATELLITE1 STAR4 PLANET14) [1] • International planning competition benchmarks

  41. Supra-Human Planning • Industrial planning • Lithographic printing production planning • Complex set of discrete & geometric constraints • Expert+ performance (40 job ganging) • Domain-specific implementation of SAT techniques

  42. Conclusion • An early goal of AI was to create programs that exhibited commonsense • This goal proved elusive • Missing efficient methods for logical & probabilistic reasoning • Lacked grounding in real world • Lacked compelling applications • Today we have the tools, the sensors, and the motivation

  43. Acknowledgements • Tongxin Bai, Jeff Bilmes, Gaetano Borriello, Tanzeem Choudhury, Kate Deibel, Oren Etzioni, Dieter Fox, Carla Gomes, Craig Harman, Harlan Hile, Eric Horvitz, Kurt Johnson, Lin Liao, Alan Liu, Joseph Modayil, Don Patterson, Parag, Sangho Park, Bill Pentney, Matthai Philipose, Yongshao Ruan, Ashish Sabharwal, Tian Sang, Bart Selman, Dan Weld, Danny Wyatt

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