1. PAGI
World:
A
simula1on
Environment
to
Challenge
Cogni1ve
Architectures
John
Licato
Selmer
Bringsjord
Rensselaer
AI
and
Reasoning
(RAIR)
Lab
2. Last
week
• John
Laird
talked
about
“interac1ve
task
learning”
• Today,
we
will
present
a
simulator
to
facilitate
such
research
3. Developmental
AI
–
Emerging
field
aOemp1ng
to
show
how,
using
an
agent
endowed
with
minimal
innate
capaci1es
embedded
in
a
sufficiently
rich
environment,
higher-‐level
cogni1ve
abili1es
can
emerge.
What
makes
an
environment
sufficiently
rich?
Guerin,
Frank
(2011).
Learning
like
a
baby:
A
survey
of
ar1ficial
intelligence
approaches.
The
Knowledge
Engineering
Review,
26(2),
209-‐236.
4. Guerin
(2011)’s
requirements
A
sufficiently
rich
environment…
C1
–
is
rich
enough
to
provide
knowledge
which
would
bootstrap
the
understandings
of
concepts
rooted
in
physical
rela1onships,
e.g.:
inside
vs.
outside,
large
vs.
small,
above
vs.
below
C2
–
can
allow
for
the
modeling
and
acquisi1on
of
spa1al
knowledge
(widely
regarded
to
be
a
founda1onal
domain
of
knowledge
acquisi1on)
through
interac1on
with
the
world.
C3
–
can
support
the
crea1on
and
maintenance
of
knowledge
which
the
agent
can
verify
itself.
5. Our
addi1onal
requirements
A
sufficiently
rich
environment…
C4
–
rich
enough
to
provide
much
of
the
sensory-‐level
informa1on
accessible
to
a
real-‐world
agent.
C5
–
allows
for
tes1ng
of
a
virtually
unlimited
number
of
tasks,
whether
they
test
low-‐level
implicit
knowledge,
high-‐level
explicit
knowledge,
or
any
of
the
other
areas
required
by
PAGI,
ideally
allowing
for
the
crea1on
of
new
tasks
without
substan1al
programming
effort.
C6
–
allows
a
wide
variety
of
AI
systems
based
on
vastly
different
theore1cal
approaches
to
aOempt
the
same
tasks,
thus
enabling
these
different
approaches
to
be
directly
compared.
CT
–
can
support
tasks
capable
of
verifying
AI
able
to
pass
the
Tailorability
Concern
6. Tailorability
Concern
–
that
[cogni1ve
systems]
deal
almost
exclusively
with
manually
constructed
knowledge
representa1ons,
using
toy
examples
and
source
knowledge
ocen
selected
solely
to
display
some
par1cular
ability.
Gentner,
Dedre
&
Forbus,
Ken
(2011).
Computa1onal
models
of
analogy.
Wiley
Interdisciplinary
Reviews:
Cogni>ve
Science,
2(3),
266-‐276.
7. Licato,
J.,
Bringsjord,
S.,
&
Govindarajulu,
N.S.
(2014).
How
models
of
crea1vity
and
analogy
need
to
answer
the
tailorability
concern.
In
Besold,
T.R.,
Kühnberger,
K.-‐u.,
Schorlemmer,
M.,
&
Smaill,
A.
(Eds.),
Computa>onal
Crea>vity
Research
:
Towards
Crea>ve
Machines.
Atlan1s
Press.
8. Drescher
(1991):
A
star1ng
point
• Cell-‐based
world
• Simple
agent
which
occupied
one
cell
• Agent
had
a
“hand”
which
could
grasp
objects
in
the
world
• Visual
field
rela1ve
to
the
agent’s
“body”
Drescher,
Gary
L.
(1991).
Made-‐Up
Minds:
A
Construc>vist
Approach
to
Ar>ficial
Intelligence.
The
MIT
Press.
9. Drescher
(1991):
A
star1ng
point
• Used
to
show
Piage1an
(construc1vist)
boOom-‐up
crea1on
of
knowledge
• Simula1on
environment
was
1ghtly
coupled
with
his
schema
mechanism
• No
realis1c
mo1on
or
physics
• World
did
not
provide
rich
source
analogs
for
e.g.
inside
vs.
outside
Drescher,
Gary
L.
(1991).
Made-‐Up
Minds:
A
Construc>vist
Approach
to
Ar>ficial
Intelligence.
The
MIT
Press.
10. C5
–
allows
for
tes1ng
of
a
virtually
unlimited
number
of
tasks,
whether
they
test
low-‐level
implicit
knowledge,
high-‐level
explicit
knowledge,
or
any
of
the
other
areas
required
by
PAGI,
ideally
allowing
for
the
crea1on
of
new
tasks
without
substan1al
programming
effort.
12. PAGI
World
can
be
run
on:
Windows
Mac
OS
Linux
(through
Chrome
browser)
Android
and
Iphone
(in
theory)
AI
can
be
wriCen
in:
ANY
programming
language
which
supports
TCP/IP
13. C1
–
is
rich
enough
to
provide
knowledge
which
would
bootstrap
the
understandings
of
concepts
rooted
in
physical
rela1onships,
e.g.:
inside
vs.
outside,
large
vs.
strong
C2
–
can
allow
for
the
modeling
and
acquisi1on
of
spa1al
knowledge
(widely
regarded
to
be
a
founda1onal
domain
of
knowledge
acquisi1on)
through
interac1on
with
the
world.
14. C3
–
can
support
the
crea1on
and
maintenance
of
knowledge
which
the
agent
can
verify
itself.
Warning:
DCEC*
is
a
highly
expressive
computa1onal
logic
and
therefore
the
cogni1on
which
it
enables
may
or
may
not
be
within
reach
of
a
given
cogni1ve
architecture.
But
PAGI
World
allows
us
to
test
and
see!
15.
16. C4
–
rich
enough
to
provide
much
of
the
sensory-‐level
informa1on
accessible
to
a
real-‐world
agent.
C6
–
allows
a
wide
variety
of
AI
systems
based
on
vastly
different
theore1cal
approaches
to
aOempt
the
same
tasks,
thus
enabling
these
different
approaches
to
be
directly
compared.
Controlled by
PAGI-side
!
!
!
!
!
!
!
!
!
!
Reflex and
State
Machine
Controlled by
AI-side
!
!
!
!
!
TCP/
IP
pyPAGI (optional)
!
!
!
DCEC*
extractor/
convertor
Physics
Engine
Task
Editor
Configurable by external user
24. From the Licato presentation in IBM’s Cognitive Systems Institute Lecture Series:
“PAGI World:A Simulation Environment to Challenge Cognitive Architectures".
For more information, visit https://www.linkedin.com/groups/Cognitive-Systems-Institute-6729452
25. PAGI
World
is
a
challenge
to
AI
and
cogni1ve
architecture
researchers
Let’s
create
tasks,
AI
systems
to
solve
them,
compare
the
approaches,
and
repeat-‐-‐-‐and
keep
this
field
moving
forward!
26. How
to
access
PAGI
World
(beta
version)
Email
John
Licato
at
licatj@rpi.edu
