Selmer Bringsjord, Bettina Schimanski Cognitive Science / Computer Science Depts.

1. An Evening with PERI, "The IQ GURU" Selmer Bringsjord, Bettina Schimanski Cognitive Science / Computer Science Depts. RAIR (Rensselaer Artificial Intelligence and Reasoning) Lab selmer@rpi.edu ; schimb@rpi.edu October 17, 2002

2. Can you solve this puzzle? In under one second? PERI can!

3. Overview •  What is Psychometric AI? • Intelligence Tests and Artificial Intelligence • Description of a 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

4. An Answer to: What is AI? A 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 entities capable of at least solid performance on all established, validated tests of intelligence and mental ability, a class of tests that includes IQ tests, tests of reasoning, of creativity, mechanical ability, 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) AI is the field devoted to building intelligent artificial agents, i.e., agents capable of solid performance on intelligence tests.

5. Intelligence Tests: Narrow vs. Broad Thurstone’s view of intelligence Spearman’s view of intelligence

6. 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)

7. Artificial Agent to Crack RPM =========== 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 ============ Correct!

8. 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)

9. 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.

10. Pie Piece Design Task Basic Setup Solution Problem

11. Block Design Task For legal reasons we cannot reveal the actual WAIS Block Design Task, so we refer to another similar Block Design task which PERI can also solve All sides are unique of all the cubes

12. 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?

13. PERI System: Flow of Information Vision System Common Lisp Program Environment Robot Speech

14. PERI(Psychometric Experimental Robotic Intelligence)

15. 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

16. How PERI Moves • Arm Pivot Points: (5 D.O.F.) • Base • Shoulder • Elbow • Wrist Bend • Wrist twist • Hand: Pneumatic Gripper (Open or Closed positions only) • Movement: Joint Coordinates vs. XYZ Coordinates

17. Joint coordinates are easier to code once a particular reference point for each axis is known, but are they easy to “guess” for dynamic programming of the robot in the future?

18. XYZ Coordinates are harder for humans to guess and must be more accurate, but may be easier for a program to calculate.

19. 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

20. A Portion of the Current 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] ;records joint values for position ARR[1] 26318 -58700 16264 27779 -2

21. Story Completion Example

22. Story Completion Example Solved

23. Story Completion(More Difficult Example) Currently untouchable AI -- but we shall see.

24. Story Completion(More Difficult Example Solved)

25. 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 events in a (twisted) narrative.

26. 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 puzzle for ALL objects YET

27. 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 such as Cognitive Science II in Spring 2003

28. Polyhedron Design Task: The “General” Case • For any 3-dimensional objects (within reason) • Objects with discrete attributes on each side • Goal state: Assemble objects together to match

29. General Algorithm for 3-D puzzle pieces: • Document original pieces by color, dimension, characteristics on each side, and total number. • Input goal configuration (a picture that will need to be deciphered). • Partition the goal into distinguishable pieces that match similar aspects of those that are available pieces in the original. Start first with the entire goal as one piece. Some aspects of the pieces may be ignored at this stage. • Once the goal has been partitioned, determine if original puzzle pieces match the partitioned ones. If not, go back to step 3 and partition into two pieces, three pieces, etc. (An exceedingly large cutoff is imposed to handle cases where no partitioning is valid, otherwise non-halting is possible). If there are matching original puzzle pieces to the goal partitioning, go on to step 5. • Start with a goal piece and match it to an original piece that has not yet been used. There will be a finite search for each matching piece since step 4 has been passed, indicating the goal is known to be solvable. When a match is found, the original piece is physically added to the solution “arena” by changing the <x,y> positioning of the original piece as well as the angle, side, or any other necessary aspect. Continue the present step until no more pieces in the goal exist that need a match.