Transcript scb.bs.10.17.2002.pai.pres

Selmer Bringsjord, Bettina Schimanski
Cognitive Science / Computer Science Depts.
RAIR (Rensselaer Artificial Intelligence and Reasoning) Lab
[email protected] ; [email protected]
October 17, 2002
 What is Psychometric AI?
 Intelligence Tests and Artificial Intelligence
 Description of the Block Design Task
 PERI, PERI, and more PERI
 Story Completion Task
 Narratological Reasoning
 Future Related Goals and Research
 Object Assembly Task
 Other tests, including TTT
AnAAnswer
to: What
is AI?
New Kind
of AI
Assume the ‘A’ part isn’t the problem: we know what
an artifact is.
 Psychometric AI offers a simple but radical answer:

– Psychometric AI is the field devoted to building information-processing
AI iscapable
the field
to building
intelligent
entities
of at devoted
least solid performance
on all
established,
validated
testsagents,
of intelligence
mentalcapable
ability, a class
of tests that
artificial
i.e., and
agents
of solid
includes IQ tests, tests of reasoning, of creativity, mechanical ability,
performance
on intelligence tests.
and
so on.
Don’t confuse this with: “Some human is
intelligent…”
 Psychologists don’t agree on what human intelligence
is.

– Two notorious conferences. See The g Factor.

But we can agree that one great success story of
psychology is testing, and prediction on the basis of
it. (The Big Test)
Psychologists don’t agree on what human
intelligence is but we can agree that one great success
story of psychology is testing, and prediction on the
basis of it.
 Definition: Some artificial agent is intelligent if and
only if it excels at all established, validated tests of
[human] intelligence.
Intelligence Tests: Narrow vs. Broad
Spearman’s
view of intelligence
Thurstone’s view of
intelligence
Artificial Agent to Crack RPM
---------------- PROOF ---------------1 [] a33!=a31.
3 [] -R3(x)| -T(x)|x=y| -R3(y)| -T(y).
16 [] R3(a31).
24 [] T(a31).
30 [] R3(a33).
31 [] T(a33).
122 [hyper,31,3,16,24,30,flip.1] a33=a31.
124 [binary,122.1,1.1] $F.
------------ end of proof ----------------------- times (seconds) ----------user CPU time
0.62
(0 hr, 0 min, 0 sec)
Artificial Agent to Crack RPM
Correct!
=========== start of search ===========
given clause #1: (wt=2) 10 [] R1(a11).
given clause #2: (wt=2) 11 [] R1(a12).
given clause #3: (wt=2) 12 [] R1(a13).
...
given clause #4: (wt=2) 13 [] R2(a21).
given clause #278: (wt=16) 287
[para_into,64.3.1,3.3.1] R2(x)| -R3(a23)|
-EmptyBar(y)| -R3(x)| -EmptyBar(x)| -T(a23)| R3(y)| -T(y).
given clause #279: (wt=16) 288
[para_into,65.3.1,8.3.1] R2(x)| -R3(a23)|
-StripedBar(y)| -R3(x)| -StripedBar(x)| EmptyBar(a23)| -R3(y)|
-EmptyBar(y).
Search stopped by max_seconds option.
============ end of search ============
 One such well-established test is the WAIS IQ test
(Wechsler Adult Intelligence Scale).
 By attempting to score relatively well on this
test with our AI Agent and being successful (as
well on others with a general algorithm), what
will this say about human intelligence, these IQ
tests, etc.?
Possible objection to the Psychometric AI:
“If one were offered a machine purported to be intelligent,
what would be an appropriate method of evaluating this
claim? The most obvious approach might be to give the
machine an IQ test … However, [good performance on
tasks seen in IQ tests would not] be completely satisfactory
because the machine would have to be specially prepared
for any specific task that it was asked to perform. The task
could not be described to the machine in a normal
conversation (verbal or written) if the specific nature of the
task was not already programmed into the machine. Such
considerations led many people to believe that the ability to
communicate freely using some form of natural language is
an essential attribute of an intelligent entity.” (Fischler &
Firschein 1990, p. 12)
Our Rebuttal:
PERI will be able to solve many puzzles, without getting
advance notice.
 PERI should specifically solve all of the WAIS!
From there, we move to all other established tests.
 PERI is not, and will not be, pre-engineered!
 Humans will communicate with PERI in natural language.
Basic
Setup
Problem
Solution
Very General Block Design Task Algorithm:
• Interpret and symbolize the initial configuration of the
pieces unless they will always be started in the same set-up.
• Do the same for the goal configuration of the pieces.
• Determine if the puzzle is solvable (there are enough pieces
available, enough with correct sides needed and colors, etc.)
• If so, pick a piece to start with and move it into position.
• Continue until the solution has been reached.
Complete Algorithm?
PERI System: Flow of Information
Vision
System
Common
Lisp
Program
Environment
Robot
Speech
PERI
(Psychometric Experimental Robotic Intelligence)
Scorbot-ER IX Robot
from the Intelitek
Corporation
• Capable of lifting objects of 4.4 lbs. or less
• Five axes (human arm has seven)
• Comes with several different software options
• ACL (Advance Control Language)
• ACL - Offline
• ATS (Advanced Terminal Software)
• Scorbase Pro
• RoboCell
How PERI Moves
• Pivot Points:
• Base
• Shoulder
• Elbow
• Wrist Bend
• Wrist twist
• Hand: Gripper (Open or Closed positions only)
•Movement: Joint Coordinates vs. XYZ Coordinates
Robot Coordinates: Joints
#
AX-1
AX-2
1
-13543
-58700
2
26318
-58700
3
-93027
-58700
4
-13543
75821
5
-13543
33980
6
-13543
-58700
7
-13543
-58700
8
-13543
-58700
9
-13543
-58700
10
-13543
-58700
11
-13543
-58700
AX-3
16264
16264
16264
16264
16264
-84239
50914
16263
16263
16263
16263
AX-4
27779
27779
27779
27778
27779
27778
27779
1930
59696
27779
27779
AX-5
-2
-2
-2
-2
-2
-2
-2
-2
-2
-56889
53229
Type
Absolute
Absolute
Absolute
Absolute
Absolute
Absolute
Absolute
Absolute
Absolute
Absolute
Absolute
Coor.
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint coordinates are easier to code but are they
easy to “guess” for dynamic programming for
the robot in the future?
Robot Coordinates: XYZ
#
X (MM)
Y (MM)
Z (MM) P (Degree)R (Degree) Type
1
313.80
-41.98
932.44
10.00
0.76 Absolute
2
240.87 -205.46
932.44
10.00
0.76 Absolute
3
168.37
268.12
932.44
10.00
0.76 Absolute
4
578.75
-41.96
82.03
-86.99
0.76 Absolute
5
664.61
-41.96
379.39
-56.82
0.76 Absolute
6
360.88
-41.97
460.96
-72.81
0.76 Absolute
7
158.73
-41.99 1017.04
38.55
0.76 Absolute
8
309.66
-41.98
842.06
-15.25
0.76 Absolute
9
265.78
-41.98 1032.76
41.17
0.76 Absolute
10
313.80
-41.98
932.43
10.00
-99.24 Absolute
11
313.80
-41.98
932.43
10.00
94.33 Absolute
Coor.
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
Joint
XYZ Coordinates are harder for humans to
guess in this case and must be more accurate,
but may be easier for a program to calculate.
Some Typical PERI Library Functions:
special-write (writes specific strings to output file which is current communication)
get-initial-setup-from-vision
get-goal-setup-from-vision
alert-piece-changed (vision system status report)
find-piece-orientation
find-piece-location
get-piece-colors
turn-piece-right
turn-piece-left
turn-piece-up
turn-piece-down
move-piece
get-new-vision-input
A portion of the Robot ACL code:
DEFP HM
;defines position HM – the HOME position
DIMP ARR[33]
;defines vector ARR: 33 positions: ARR[1] - ARR[33]
HOME
;drives all robot axes to their home position by
;searching for a microswitch on each axis
SETPV HM
;records joint values for position HM
-13543
-58700
16264
27779
-2
SETPV ARR[1]
26318
-58700
16264
27779
-2
;records joint values for position ARR[1]
PERI VIDEO
Picture Completion
Picture Completion
Narratological Reasoning &
Thwarting Terrorism…
Terrorists struggle to make stories real.
Their behavior can be anticipated, and thus
thwarted.
We need computers that can imagine future
cvents in a (twisted) narrative.
Disadvantages without the Vision System
• Blocks must be set-up the same way each time or input
differently from a file
•Input file tells set-up of all blocks including color,
orientation, position
• No dynamic feedback to Brain-program
• == cannot solve general puzzle YET
Future Goals for PERI
• Combine the CL Program, Vision Software, Robotic
Software, and Speech system into one working
package
• Tackle the Story Completion Task. What would this
mean if we’re successful? And for other tests? (abstract
algorithms for any such problems)
• Using PERI for instruction in classes
Object Assembly Task – the General Case
• Want to be able to solve the general object assembly task
• Any number of objects of any shape, color, orientation
• Any goal configuration
General Algorithm for any type of puzzle pieces: (not necessarily a complete alg.)
1) Document the number of original pieces by color, dimension, size, and number of sides.
Number the pieces in order when input. Create a flag for each that is initially not flagged.
This will be used later to determine if the piece is already used in the solution. (Flagging is
for non-vision input)
2) Input the goal configuration similarly to the original pieces. Also number each piece and
create a flag for each. When a goal piece is not flagged, this will be used to signify whether
the piece has been put in the solution already. (Flagging is for non-vision input)
3) Partition the goal into pieces that match available pieces in the original. (I.e. If there is a
piece that does not match the dimensions of any original piece, try and break it up into pieces
that will match smaller original pieces). Depending on the type of puzzle, you may be able to
ignore the color of the pieces for now.
4) Once all pieces in the goal have been partitioned, make sure the goal is solvable (i.e. there
are enough pieces with the right matching colors as the original pieces). If not, it is possible
the goal has not been partitioned correctly. Either declare defeat or go back to step 3 and
partition differently, making sure not to partition exactly like an earlier partitioning. If a
partitioning of the goal is solvable, continue onto step 5.
5) Start with the lowest numbered goal piece that is not yet flagged. Now match it to an
original piece that is also not yet flagged. Once such a match is found (which is a finite
search since coming from step 4 the goal is known to be solvable) set the flag for the original
piece and the goal piece and add the piece to the solution array by changing the coordinates
of the original piece as well as the angle if this is an option.
6) Continue step 5 until no more pieces in the goal exist that are not flagged.