Living a (Non-Schizophrenic)
Symbiosis Between Philosophy,
Psychology, and Computer Science
Selmer Bringsjord
Director, Minds & Machines Laboratory
Prof of Logic, Philosophy, Cognitive Science, and Computer Science
Dept of Philosophy, Psychology, & Cognitive Science
Department of Computer Science
Rensselaer Polytechnic Institute (RPI)
Troy NY 12180 USA
Chief Scientist
xxxxxxxxx
1223 Peoples Ave. Troy NY 12180 USA
selmer@rpi.edu
http://www.rpi.edu/~brings
Modus Operandi
• Know, really know, a paradigm that can cut across
disciplines.
• Critically examine everything, however sacred,
leveraging the fact you’ll often be an “outsider.”
• Continuously ask the “big questions,” and work from
them to engineering – not the other way around.
• Given that it would be silly to acquire multiple PhDs,
go for the equivalent by diving in (pedagogically
first, perhaps), always maintaining a “lifeline” to your
paradigm.
– What major issues in field X might yield to techniques
based in field Y (or to your cross-cutting paradigm)?
Examples, Examples…
There are many…
I’ll pick a few; we can discuss them, and others.
Example #1:
Psychometric AI
Seeking to Impact a # of Fields
• This work weaves together relevant
parts of:
– Artificial Intelligence: Build machine
agents to “crack” and create tests.
– Psychology: Use experimental methods
to uncover nature of human reasoning
used to solve test items.
– Philosophy: Address fundamental “big”
questions, e.g., What is intelligence?
Would a machine able to excel on
certain tests be brilliant?…
– Education: Discover the nature of tests
used to make decisions about how
students are taught what, when.
– Linguistics: Reduce reasoning in
natural language to computation.
Many applications!
New Kind
Kind
of
Playing Off of
Forgotten
Answer
to: What is AI?
AAaNew
ofAI
AI
• Assume the ‘A’ part isn’t the problem: we know what
an artifact is.
• Psychometric AI plays
is off a simple but radical answer:
PsychometricAI
AIisisthe
thefield
fielddevoted
devotedtotobuilding
buildinginformation-processing
information processing
– Psychometric
AI
is
the
field
devoted
to
building
intelligent
entitities
(some
which
will
robots)
capable
least
solid
entities
(some
of of
which
will
bebe
robots)
capable
of of
at at
least
solid
performance
validated
tests
artificial on
agents,
i.e., agents
capable
of solidand
performance
onall
allestablished,
established,
validated
testsofofintelligence
intelligence
andmental
ability,
aability,
class ofa tests
that
includes
IQ tests,IQ
tests
of reasoning,
of
mental
class
ofintelligence
tests
that includes
tests,
tests of reasoning,
performance
on
tests.
creativity, mechanical ability, and so on.
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/education on the
basis of it.
Therefore…
Intelligence Tests: Narrow vs. Broad
Spearman’s
view of intelligence
Thurstone’s view of
intelligence
Let’s look @ RPM
(Sample 1)
RPM Sample 2
RPM Sample 3
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
---------------- PROOF ---------------1 [] a33!=a31.
7 [] -R3(x)| -StripedBar(x)|x=y| -R3(y)| StripedBar(y).
16 [] R3(a31).
25 [] StripedBar(a31).
30 [] R3(a33).
32 [] StripedBar(a33).
128 [hyper,32,7,16,25,30,flip.1] a33=a31.
130 [binary,128.1,1.1] $F.
------------ end of proof ----------------------- times (seconds) ----------user CPU time
0.17
(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 ============
Possible Objection
“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)
WAIS
A Broad Intelligence Test…
Cube Assembly by Intelligent Agents
(thanks to Bettina Schimanski)
Basic Setup
Problem:
Solution:
The robot in Selmer’s lab that will be able to excel on the WAIS and other tests. We
don’t yet have a name for our artificial master of tests. MIT has COG. What should the
name be? Suggestions are welcome! Send to selmer@rpi.edu.
Picture Completion
Cracking Algorithm Applied to “Lobster”
Lobsters usually develop one smaller, cutter claw and one larger,
crusher claw. To show that exercise determines which claw becomes
the crusher, researchers placed young lobsters in tanks and repeatedly
prompted them to grab a probe with one claw – in each case always
the same, randomly selected claw. In most of the lobsters the grabbing
claw became the crusher. But in a second, similar experiment, when
lobsters were prompted to use both claws equally for grabbing, most
matured with two cutter claws, even though each claw was exercised
as much as the grabbing claws had been in the first experiment.
Which of the following is best supported by the information above?
A
B
C
D
E
Young lobsters usually exercise one claw more than the other.
Most lobsters raised in captivity will not develop a crusher claw
Exercise is not a determining factor in the development of crusher claws in lobsters.
Cutter claws are more effective for grabbing than are crusher claws.
Young lobsters that do not exercise either claw will nevertheless usually develop
one crusher and one cutter claw.
Option A in Lobster (v3) Verified
formula_list(usable).
all x (Lobster(x)->Cutter(l(x))&Crusher(r(x))|Cutter(r(x))&Crusher(l(x))).
all x (Lobster(x)-> -(Cutter(l(x))&Crusher(l(x)))& -(Cutter(r(x))&Crusher(r(x)))).
all x (Lobster(x)-> (Ex(r(x),l(x))->Cutter(l(x))&Crusher(r(x)))).
all x (Lobster(x)-> (Ex(l(x),r(x))->Cutter(r(x))&Crusher(l(x)))).
all x (Lobster(x)-> (-Ex(r(x),l(x))& -Ex(l(x),r(x))->Cutter(l(x))&Cutter(r(x)))).
-(all x (Lobster(x)->Ex(r(x),l(x))|Ex(l(x),r(x)))).
end_of_list.
Option A negated
---------------- PROOF ---------------4 [] -Lobster(x)|Crusher(r(x))|Crusher(l(x)).
5 [] -Lobster(x)| -Cutter(l(x))| -Crusher(l(x)).
6 [] -Lobster(x)| -Cutter(r(x))| -Crusher(r(x)).
11 [] -Lobster(x)|Ex(r(x),l(x))|Ex(l(x),r(x))|Cutter(l(x)).
12 [] -Lobster(x)|Ex(r(x),l(x))|Ex(l(x),r(x))|Cutter(r(x)).
13 [] -Ex(r($c1),l($c1)).
14 [] -Ex(l($c1),r($c1)).
15 [] Lobster($c1).
16 [hyper,15,12,unit_del,13,14] Cutter(r($c1)).
17 [hyper,15,11,unit_del,13,14] Cutter(l($c1)).
18 [hyper,15,4] Crusher(r($c1))|Crusher(l($c1)).
19 [hyper,18,6,15,16] Crusher(l($c1)).
Option A verified
21 [hyper,19,5,15,17] $F.
------------ end of proof -------------
Option B in Lobster (v3) Falsified
formula_list(usable).
all x (Lobster(x)->Cutter(l(x))&Crusher(r(x))|Cutter(r(x))&Crusher(l(x))).
all x (Lobster(x)-> -(Cutter(l(x))&Crusher(l(x)))& -(Cutter(r(x))&Crusher(r(x)))).
all x (Lobster(x)-> (Ex(r(x),l(x))->Cutter(l(x))&Crusher(r(x)))).
all x (Lobster(x)-> (Ex(l(x),r(x))->Cutter(r(x))&Crusher(l(x)))).
all x (Lobster(x)-> (-Ex(r(x),l(x))& -Ex(l(x),r(x))->Cutter(l(x))&Cutter(r(x)))).
-(all x (Lobster(x)&Captivity(x)->Cutter(l(x))&Cutter(r(x)))).
end_of_list.
Option B negated
=========== start of search ===========
given clause #1: (wt=2) 14 [] Lobster($c1).
given clause #2: (wt=2) 15 [] Captivity($c1).
given clause #3: (wt=6) 18 [hyper,14,4] Crusher(r($c1))|Crusher(l($c1)).
given clause #4: (wt=6) 19 [hyper,14,3] Crusher(r($c1))|Cutter(r($c1)).
given clause #5: (wt=6) 20 [hyper,14,2] Cutter(l($c1))|Crusher(l($c1)).
given clause #6: (wt=13) 16 [hyper,14,12] Ex(r($c1),l($c1))|Ex(l($c1),r($c1))|Cutter(r($c1)).
given clause #7: (wt=6) 21 [hyper,14,1] Cutter(l($c1))|Cutter(r($c1)).
given clause #8: (wt=13) 17 [hyper,14,11] Ex(r($c1),l($c1))|Ex(l($c1),r($c1))|Cutter(l($c1)).
given clause #9: (wt=10) 24 [hyper,17,13,16,factor_simp,factor_simp] Ex(r($c1),l($c1))|Ex(l($c1),r($c1)).
given clause #10: (wt=8) 25 [hyper,24,8,14] Ex(l($c1),r($c1))|Crusher(r($c1)).
given clause #11: (wt=8) 26 [hyper,24,7,14] Ex(l($c1),r($c1))|Cutter(l($c1)).
given clause #12: (wt=8) 27 [hyper,24,10,14] Ex(r($c1),l($c1))|Crusher(l($c1)).
Option B falsified
given clause #13: (wt=8) 28 [hyper,24,9,14] Ex(r($c1),l($c1))|Cutter(r($c1)).
Search stopped because sos empty.
============ end of search ============
Example #2:
Humans as Evolved Machines?
Argument #2…
Genetic Algorithm
begin GA
g:= 0
Initialize population P(g)
Evaluate population P(g) (i.e., compute fitness values)
while not done do
g:=g+1
Select P(g) from P(g-1)
Crossover P(g)
Mutate P(g)
Evaluate P(g)
end while
end GA
Simple Example
Label
A
B
C
D
Genome
00000110
11101110
00100000
00110100
Fitness
2
6
Av Fitness:
1
3
3
Assume parents {B,D} and {B,C} and crossover at first bit for
{B,D} forming E and F, and none for {B,C}. Then:
Label
E
F
B
C
Genome
10110100
01101110
11101110
00100000
Fitness
4
5
6
1
Proceeding…
Assume offspring E is mutated at the 6th position to form E’,
Offspring B is mutated at the first bit position for form B’, and
Offspring F and C are not mutated at all.
Label
E’
F
B’
C
Genome
10110000
01101110
01101110
00100000
.
.
.
Fitness
3
5
5
1
Av Fitness:
144
Moral of the story…
The process of evolution is an
algorithm, or mechanical procedure,
or computation.
The Computational Landscape
Space of all information processes
Analog Chaotic Neural
Networks, Zeus Machines,
Weyl Machines, P/Poly
Machines, …
Hypercomputation
The “Turing Limit”
Turing Machines, Algorithms, Programs, …
Theorem Ã
No algorithmic process can produce a
device capable of hypercomputation.
The “Fragility” of the Proposition that
People are the Product of Evolution
(1) If people are the product of evolution, they were
produced by an algorithmic process (= by a process
at or below the Turing Limit).
(2) Theorem Ã: No algorithmic process can produce a
device capable of hypercomputation.
(3) People are capable of hypercomputation.
(4)  People were not produced by an algorithmic
process. (from 3 and 4)
(5)  People aren’t the product of evolution.
(from 4 and 1)
Example #3:
Reaching Simon’s Dream
The Primacy of Psychology of Reasoning
There is consensus among the relevant luminaries in AI and theorem proving
and psychology of reasoning and cognitive modeling that: machine
reasoning stands to the best of human reasoning as a rodent stands to the
likes of Kurt Godel. In the summer before Herb Simon died, in a
presentation at CMU, he essentially acknowledged this fact -- and set out
to change the situation by building a machine reasoner with the power of
first-rate human reasoners (e.g., professional logicians). Unfortunately,
Simon passed away. Now, the only way to fight toward his dream (which of
course many others before him expressed) is to affirm the primacy of
psychology of reasoning. Otherwise we will end up building systems that
are anemic. The fact is that first-rate human reasoners use techniques
that haven't found their way into machine systems. E.g., humans use
extremely complicated, temporally extended mental images and associated
emotions to reason. No machine, no theorem prover, no cognitive
architecture, uses such a thing. The situation is different than chess -radically so. In chess, we knew that brute force could eventually beat
humans. In reasoning, brute force shows no signs of exceeding human
reasoning. Therefore, unlike the case of chess, in reasoning we are going
to have to stay with the attempt to understand and replicate in machine
terms what the best human reasoners do.
Multi-Agent Reasoning, modeled in
“Chess
Mental Metalogic, is the key
is
Too
to reaching Simon’s Dream!
Easy”
Pilot experiment shows that groups
of reasoners instantly surmount
the errors known to plague individual
reasoners!
Herb Simon’s Dream
Example #4:
Cultivating “System 2” Cognition
J-L #1
Suppose that the following premise is true:
If there is a king in the hand, then there is an ace
in the hand, or else if there isn’t a king in the hand,
then there is an ace.
What can you infer from this premise?
NO!
There is an ace in the hand. NO!
In fact, what you can infer is that there isn’t an ace in the hand!
Problem Given Selmer on Train
Everyone loves anyone who loves someone.
Alvin loves Bill.
Can you infer that everyone loves Bill?
ANSWER:
JUSTIFICATION:
Problem Given Selmer on Train
Everyone loves anyone who loves someone.
Alvin loves Bill.
Can you infer that everyone loves Bill?
ANSWER: Yup.
JUSTIFICATION: See proof just given.
Harder Variants
Everyone loves anyone who loves someone.
Alvin doesn’t love Bill.
Can you infer that Cath loves Bill?
Can you infer that Cath loves Dave?
ANSWER:
JUSTIFICATION:
Harder Variants
Everyone loves anyone who loves someone.
Alvin doesn’t love Bill.
Can you infer that Cath loves Bill?
Can you infer that Cath loves Dave?
ANSWER: Yes x 2
JUSTIFICATION: See proof just given.
A Technological Fix?
NSF-sponsored Development of
Artificial Tutoring Agents
Example #5:
Foundations of AI
Rather obviously, human persons aren’t
standard computers (the speed fetish)
If R5 is non-empty,
take a bead away.
,
,
,
R1 R2 R3 R4 R5 R6 R7 R8 R9
Confess to Having Little Respect
for Computing Machines
super-rational adults
rational adults
rats
…
dogs
dolphins monkeys children adults
computers/robots
simulate all behaviors
computers/robots
simulate some reasoning
King-Ace
and the like
Infinitary
Paradoxes!
uperminds
Strong AI
(we are machines
and we can literally
be replicated in standard
computing machines)
Hans Moravec
Most AIniks?
Bill Joy
replication
simulation
Me
Weak AI
(we are more than
computing machines, but
our behavior can be, to
some degree, simulated
by such machines)
Panglossian AI
John Searle
Fatalistic AI
Dark Side of AI Forever Fiction
Classes Taught
•
•
•
•
•
•
•
•
•
Introduction to Logic
Psychology of Reasoning
Cognitive Psychology
Introduction to Cognitive Science
Introduction to Artificial Intelligence
Computability and Logic
Logic and AI (grad seminar)
Advanced Logic courses
Introduction to Philosophy
Modus Operandi
• Know, really know, a paradigm that can cut across
disciplines.
• Critically examine everything, however sacred,
leveraging the fact you’ll often be an “outsider.”
• Continuously ask the “big questions,” and work from
them to engineering – not the other way around.
• Given that it would be silly to acquire multiple PhDs,
go for the equivalent by diving in (pedagogically
first, perhaps), always maintaining a “lifeline” to your
paradigm.
– What major issues in field X might yield to techniques
based in field Y (or to your cross-cutting paradigm)?
The End
Example #6:
Engineering Creative Machines
But Specifically on Our Topic:
Lady Lovelace, long ago:
“Computers can’t be creative. For to
be creative is to originate something.
But computers originate nothing.
They merely follow the programs
given to them.”
But you built this system called
BRUTUS over a period of seven
years, and BRUTUS is creative,
no?
Well, Our Aim is to Engineer a System
Able to Prevail Against a Human in S3G
Judge
A story…
A story….
Building Literarily Creative Computing
Machines: Some Engineering Challenges
• Formalizing Literary Themes
– For me it’s been betrayal
– Coming: mendacity
– “Selmer, we want X in our game.”
• Well, I need some serious money for that.
• Story Mastery
– Without it, hack-and-slash, at best
– The Bates experiment
– Fortunes to be made here
• Building Robust Autonomous Characters
• Personalization
Mendacity
Logical Systems: Which for Machine Creativity?
Name
Alphabet
Grammar
Proof
Theory
Semantics
Metatheory
LPC
p, q, r, … and
truthfunctional
connectives
Easy
Fitch-style and
natural
deduction,
resolution, etc.
Truth tables!
Sound,
complete,
compact,
decidable
Add variables
x, y, … and 

Easy
Fitch-style and
natural
deduction,
resolution, e.g.
Structures and
interpretations
Sound,
complete,
compact,
undecidable
LPML
Add “box” and
“diamond” for
necessity and
possibility
Wffs created
by prefixing
new operators
to wffs
Add
necessitation,
etc.
Possible
worlds
Similar to
LPC
LII
New variables
for predicates
Pretty obvious
New adapt
quantifier
rules
Quantification
over subsets in
domain
allowed
Sound but not
complete
Propositional Calculus
LI
First-Order Logic
Unfortunately…
Name
Alphabet
Grammar
Proof
Theory
Semantics
Metatheory
LPC
p, q, r, … and
truthfunctional
connectives
Easy
Fitch-style and
natural
deduction,
resolution, etc.
Truth tables!
Sound,
complete,
compact,
decidable
Add variables
x, y, … and 

Easy
Fitch-style and
natural
deduction,
resolution, e.g.
Structures and
interpretations
Sound,
complete,
compact,
undecidable
LPML
Add “box” and
“diamond” for
necessity and
possibility
Wffs created
by prefixing
new operators
to wffs
Add
necessitation,
etc.
Possible
worlds
Similar to
LPC
LII
New variables
for predicates
Pretty obvious
New adapt
quantifier
rules
Quantification
over subsets in
domain
allowed
Sound but not
complete
Propositional Calculus
LI
First-Order Logic
There is an empirical way to
express Lovelace’s challenge:
what I call the…
Lovelace Test
Like Kasparov vs. Deep
Blue…
Joel Benjamin
Murray Campbell
…
Kasparov
In the TT, it’s Really Judge vs. Designer
I can handle tha- uh, it
can handle that one.
Judge
What color and in what
style is your hair?
Designer
The Lovelace Test
How did it do that?
S
o
Designer (= Judge)
Judge (= Designer)
Definition of Lovelace Test
• Artificial agent A, designed by H, passes LT if and
only if
– A outputs o;
– A’s outputting o is not the result of a fluke hardware error,
but rather the result of processes A can repeat;
– H (or someone who knows what H knows, and has H’s
resources) cannot explain how A produced o by appeal to
A’s architecture, knowledge-base, and core functions.
Intelligent Agents
Generic Knowledge-Based Agent
(smarter than what appear in nearly all games,
including, e.g., Hitman)
function KB-Agent(percept) returns an action
inputs: p, a percept
static: KB, a knowledge base
t, a counter, initially 0
Tell(KB, Make-Percept-Sentence(percept, t))
action  Ask(KB, Make-Action-Query(t))
Tell(KB, Make-Action-Sentence(action, t))
tt+1
return action
What Systems Fail LT?
• Brutus (see final chapter)
• Copycat (see book as well)
• Letter Spirit
.
.
.
Is the Set of All “A”s Countable?
The Original Dream
ABCDEF…Z
Percepts: ?
Actions:
Design remaining
letters
Letter Spirit System as an
Intelligent Agent
Letter Spirit
ABC
Percepts:
seed letters
DEF…Z
Actions:
Design remaining
letters
The Retreat to Grids
Ten Human-Designed Gridfonts
1500 “A”s are Possible
But If We Want Genuine
Machine Creativity?
• Exploiting what it’s like to, say, find a dead
body in a…
Toward Mendacity
• x tells lie p to y iff
– p is false;
– x knows that p is false;
Toward Mendacity
• x tells lie p to y iff
– p is false;
– x knows that p is false;
• But where’s the communication?
Toward Mendacity
• x tells lie p to y iff
– x (in some technical communicative sense) tells
y p;
• (Using AIMA, we could invoke TELLing to
another’s KB)
– p is false;
– x knows that p is false;
Toward Mendacity
• x tells lie p to y iff
– x (in some technical communicative sense) tells
y p;
• (Using AIMA, we could invoke TELLing to
another’s KB)
– p is false;
– x knows that p is false;
• But perhaps x is being sarcastic!
Toward Mendacity
• x tells lie p to y iff
– x (in some technical communicative sense) tells
y p;
• (Using AIMA, we could invoke TELLing to
another’s KB)
– p is false;
– x knows that p is false;
– x wants y to believe p.
Toward Mendacity
• x tells lie p to y iff
– x (in some technical communicative sense) tells y p;
• (Using AIMA, we could invoke TELLing to another’s KB)
– p is false;
– x knows that p is false;
– x wants y to believe p.
• Does this do it?
Back: Some Key Challenges
Example #7:
Multi-agent “proof battles”
…
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items
Multi-Agent Proof: Substrate for RC Items