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  1. Announcements • HW 6: Written (not programming) assignment. • Assigned today; Due Friday, Dec. 9. E-mail to me. • Quiz 4 : OPTIONAL:Take home quiz, open book. • If you’re happy with your quiz grades so far, you don’t have to take it. (Grades from the four quizzes will be averaged.) • Assigned Wednesday, Nov. 30; due Friday, Dec. 2 by 5pm. (E-mail or hand in to me.) • Quiz could cover any material from previous quizzes. • Quiz is designed to take you one hour maximum (but you have can work on it for as much time as you want, till Friday, 5pm).

  2. Topics we covered • Turing Test • Uninformed search • Methods • Completeness, optimality • Time complexity • Informed search • Heuristics • Admissibility of heuristics • A* search

  3. Game-playing • Notion of a game tree, ply • Evaluation function • Minimax • Alpha-Beta pruning • Natural-Language Processing • N-grams • Naïve Bayes for text classification • Support Vector Machines for text classification • Latent semantic analysis • Watson question-answering system • Machine translation

  4. Speech Recognition • Basic components of speech-recognition system • Perceptrons and Neural Networks • Perceptron learning and classification • Multilayer perceptron learning and classification • Genetic Algorithms • Basic components of a GA • Effects of parameter settings • Vision • Content-Based Image Retrieval • Object Recogition

  5. Analogy-Making • Basic components of Copycat, as described in the slides and reading • Robotics • Robotic Cars (as described in the reading) • Social Robotics (as described in the reading)

  6. Reading for this week(links on the class website) S. Thrun, Toward Robotic Cars C. Breazeal, Toward Sociable Robotics R. Kurzweil, The Singularity is Near: Book Precis D. McDermott, Kurzweil's argument for the success of AI

  7. Robotic Cars • • •

  8. From S. Thrun, Towards Robotic Cars

  9. Examples of Components of Stanley / Junior • Localization: Where am I? • Establish correspondence between car’s present location and a map. • GPS does part of this but can have estimation error of > 1 m. • To get better localization, relate features visible in laser scans to map features.

  10. Examples of Components of Stanley / Junior • Obstacles: Where are they? • Static obstacles: Build “occupancy grid maps”

  11. Moving obstacles: Identify with “temporal differencing” with sequential laser scans, and then use “particle filtering” to track • “Particle filter” – related to Hidden Markov Model

  12. Particle Filters for Tracking Moving Objects From

  13. Examples of Components of Stanley / Junior • Path planning: • “Structured navigation” (on road with lanes): • “Junior used a dynamic-programming-based global shortest path planner, which calculates the expected drive time to a goal location from any point in the environment. Hill climbing in this dynamic-programming function yields paths with the shortest expected travel time.”

  14. From M. Montemerlo et al., Junior: The Stanford Entry in the Urban Challenge

  15. Examples of Components of Stanley / Junior • “Unstructured navigation” (e.g., parking lots, u-turns) • Junior used a fast, modified version of the A* algorithm.This algorithm searches shortest paths relative to the vehicle’s map, using search trees.

  16. From M. Montemerlo et al., Junior: The Stanford Entry in the Urban Challenge

  17. Examples of Components of Stanley / Junior

  18. New York Times: “Google lobbies Nevada to allow self-driving cars”

  19. Sociable Robotics

  20. Kismet Kismet and Rich

  21. What can Kismet do?

  22. What can Kismet do? • Vision • Visual attention • Speech recognition (emotional tone) • Speech production (prosody) • Speech turn-taking • Head and face movements • Facial expression • Keeping appropriate “personal space”

  23. Overview and Hardware

  24. Expressions examples

  25. From Recognition of Affective Communicative Intent in Robot-Directed Speech C. BREAZEAL AND L. ARYANANDA Perceiving “affective intent”

  26. From Recognition of Affective Communicative Intent in Robot-Directed Speech C. BREAZEAL AND L. ARYANANDA Perceiving “affective intent”

  27. Perceiving affective intent

  28. From A context-dependent attention system for a social robot C. Breazeal and B. Scassellati Vision system

  29. From External influences on attention Weighted by behavioral relevance • Attention is allocated according to salience • Salience can be manipulated by shaking an object, bringing it closer, moving it in front of the robot’s current locus of attention, object choice, hiding distractors, … Current input Skin tone Saliency map Color Motion Habituation Pre-attentive filters

  30. Vision System: Attention

  31. From Internal influences on attention “Seek toy” – low skin gain, high saturated-color gain Looking time 28% face, 72% block “Seek face” – high skin gain, low color saliency gain Looking time 28% face, 72% block • Internal influences bias how salience is measured • The robot is not a slave to its environment

  32. Attention: Gaze direction

  33. Attention System

  34. From Negotiating interpersonal distance • Robot establishes a “personal space” through expressive cues • Tunes interaction to suit its vision capabilities Person backs off Person draws closer Beyond sensor range Too far – calling behavior Too close – withdrawal response Comfortable interaction distance

  35. Negotiating personal space

  36. From Negotiating object showing • Robot conveys preferences about how objects are presented to it through irritation, threat responses • Again, tunes interaction to suit its limited vision • Also serves protective role Comfortable interaction speed Too fast – irritation response Too fast, Too close – threat response

  37. Negotiating object showing

  38. Adapted from Turn-Taking • Cornerstone of human-style communication, learning, and instruction • Phases of turn cycle • Listen to speaker: hold eye contact • Reacquire floor: break eye contact and/or lean back a bit • Speak: vocalize • Hold the floor: look to the side • Stop one’s speaking turn: stop vocalizing and re-establish eye contact • Relinquish floor: raise brows and lean forward a bit

  39. Conversational turn-taking

  40. Web page for all these videos:

  41. How to evaluate Kismet? What are some applications for Kismet and its descendants?

  42. Leonardo