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).

1

Topics we covered

• Turing Test

• Uninformed search– Methods– Completeness, optimality– Time complexity

• Informed search– Heuristics– Admissibility of heuristics– A* search

2

• 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

3

• 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

4

• 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)

5

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

6

Robotic Cars

7

• http://www.ted.com/talks/sebastian_thrun_google_s_driverless_car.html

• http://www.youtube.com/watch?v=lULl63ERek0

• http://www.youtube.com/watch?v=FLi_IQgCxbo

8

From S. Thrun, Towards Robotic Cars

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.

9

• Obstacles: Where are they?– Static obstacles: Build “occupancy grid maps”

10

Examples of Components of Stanley / Junior

– Moving obstacles: Identify with “temporal differencing” with sequential laser scans, and then use “particle filtering” to track

– “Particle filter” – related to Hidden Markov Model

11

12

Particle Filters for Tracking Moving Objects

From http://cvlab.epfl.ch/teaching/topics/

• 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.”

13

Examples of Components of Stanley / Junior

14

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

– “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.

15

Examples of Components of Stanley / Junior

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”

http://www.nytimes.com/2011/05/11/science/11drive.html

19

20

Sociable Robotics

21

Kismet

Kismet and Rich

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

24

Overview and Hardware

25

Expressions examples

26

From Recognition of Affective Communicative Intent in Robot-Directed SpeechC. BREAZEAL AND L. ARYANANDA

Perceiving “affective intent”

27

From Recognition of Affective Communicative Intent in Robot-Directed SpeechC. BREAZEAL AND L. ARYANANDA

Perceiving “affective intent”

28

Perceiving affective intent

29

From A context-dependent attention system for a social robotC. Breazeal and B. Scassellati

Vision system

Skin tone Color Motion Habituation

Weightedby behavioral

relevance

Pre-attentive filters

External influences on attention

• 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 Saliency map

From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

31

Vision System: Attention

“Seek face” –high skin gain, low color saliency gainLooking time 28% face, 72% block

“Seek toy” –low skin gain, high saturated-color gain

Looking time 28% face, 72% block

Internal influences on attention

Internal influences bias how salience is measured The robot is not a slave to its environment

From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

33

Attention: Gaze direction

34

Attention System

Comfortable interaction distance

Too close – withdrawal response

Too far – calling

behavior

Person draws closer

Person backs off

Beyond sensor range

Negotiating interpersonal distance

• Robot establishes a “personal space” through expressive cues

• Tunes interaction to suit its vision capabilities

From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

36

Negotiating personal space

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

From people.csail.mit.edu/paulfitz/present/social-constraints.ppt

38

Negotiating object showing

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

Adapted from people.csail.mit.edu/paulfitz/present/social-constraints.ppt

Conversational turn-taking

Web page for all these videos:

http://www.ai.mit.edu/projects/sociable/videos.html

41

How to evaluate Kismet?

What are some applications for Kismet and its descendants?

42

Leonardo

http://www.youtube.com/watch?v=ilmDN2e_Flc

43