Modeling Human Intelligence as a Slow Intelligence System

1
Modeling Human Intelligence as a Slow
Intelligence System

• Tiansi Dong
• Department of Computer Science
• University of Hagen
• Germany

2
Outline

• Slow Intelligence System (SIS)
• Properties of Human Intelligence
• The question
• Case study in Spatial Reasoning
• Within one snapshot view
• Between snapshot views
• Conclusion
• Outlooks

3
Slow Intelligence System

• Solve problems by trying
• Context-aware
• May not perform well in a short run
• Learn to improve its performance

4
Slow Intelligence System
environment
propagator
concentrator
enumerator
adaptor
eliminator
Solution
Problem
timing controller
environment
5
Human Intelligence is_a Slow Intelligence

• Slow developmental

professors
doctors
students
pupils
infants
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Properties of Human Intelligence
Babies cannot see constant objects
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Properties of Human Intelligence
Now suppose it not about apple, rather
football
money
bus
Spatial cognition is foundamental
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Question

• Human intelligence is_a Slow Intelligence System
• Spatial intelligence is foundamental to human
  intelligence
• Slow Intelligence System has_a architechture
• Is it possible that spatial intelligence be
  simulated within the SIS architechture?

9
SIS for Spatial Knowledge within a Scene

• A picture on a wall
• A lady in the picture
• The lady is back to us
• A gentalman is near the picture
• The gentalman is at the left side of the picture

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SIS for Spatial Knowledge within a Scene

• Object categories
• A picture
• A lady
• A wall
• Spatial relations
• On, in
• Back, left
• Near

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Specific Question

• Object categories
• A picture
• A lady
• A wall

• Cross linguistic spatial relations
• in, on, near, front, left,...
• ?,?,?

?
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Results in Psychology

• Connection relation is primitive
• Orientation and distance relations are acquired
• Piaget (1954) The Construction of Reality in the
  Child. Routledge Kegan Paul Ltd.
• Carey (2009) The Origin of Concepts. Oxford Press

13
Some existing work

• Neural Network
• Terry Regier (1996) The Human Semantic
  Potential, MIT Press.
• Spatial model is point-based
• 'connection' is not primitive
• Formal logic
• De Laguna (1922) Point, line and surface as sets
  of solids, The Journal of Philosophy
• T Dong (2008) Comment on RCCFrom RCC to RCC,
  Journal of Philosophical Logic
• Spatial model is region-based
• 'connection' is primitive

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Case study in Spatial Reasoning in SIS
Object categories
Connection relation
Context-aware Problem-solving by trying
near, in, on, left, right, ...
15
Spatial Reasoning for 'one foot away'
B
Trying all possible extension (problem solving by
trying), and see whether one foot connects with
the target object (context awareness).
A
?foot foot ? FOOT ? C(A, foot) ? C(foot, B)
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Spatial Reasoning for distance in SIS

• In the UK A is one foot away from B means
  region B can be reached by a region of the same
  size as the British imperial foot from A.
• China and Egypt
• Cun the body segment between the wrist striation
  behind the thumb and the pulsing point of the
  radial artery
• Cubit the segment between the bent elbow and the
  point of extended middle finger.
• In modern physics meter, light-year.
• The meter is the distance traveled by light in
  vacuum during a time interval of 1/299 792 458 of
  a second

A
B
X
Y
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Spatial Reasoning for distance comparison
A is nearer to B than to C there is an X such
that C(A, X) ? C(X, B) and there is no X such
that C(A, X) ? C(X, B).
Trying all possible extension (problem solving by
trying), and see whether one x connects with B
and non of x connects with C (context awareness).
B
X
x
A
C
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Spatial Reasoning for orientation

• A is in front of B A is nearer to the front
  part of B than to its other parts.

B
A
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Spatial Reasoning for orientation

• Orientation is determined by the shape of the
  reference object, and the method of distance
  comparison.

N
NE
NW
Reference Object
W
E
SW
S
SE
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Spatial Reasoning Performance

• Performance in term of the accuracy increases, as
  the number of sides of the reference object
  increases.

Qualitative spatial orientation frameworks, e.g.
Frank (1992), Freksa (1992), Hernández (1994),
Freksa (1999) Renz and Mitra (2004), Dong and
Guesgen (2007)
Quantitative spatial orientation
frameworks, Euclidean geometry
21
Spatial Reasoning Performance
P ae-i?
The orientation of P can be defined as the point
on the unit circle which is nearest to P.
Q e-i?
W
?
O
1
22
SIS for Spatial Reasoning for one scene Short
Summary
Context-aware (Object, Connection) Always trying
(do spatial extension) Continuously improve
performance (do adaptation)
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SIS for Spatial Reasoning between
snapshotsObject tracing
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SIS for Spatial Reasoning between
snapshotsObject tracing
25
SIS for Spatial Reasoning between
snapshotsObject tracing
26
SIS for Spatial Reasoning between sceneObject
tracing

• Fast changining leads to an illusion
• bird ? rabbit, rabbit ? bird
• Otherwise, bird flies, rabbit moves

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SIS for Spatial Reasoning between sceneObject
tracing

• A problem of object mapping between scenes
• Two object tracing results due to two different
  priorities
• Priority on spatial changes (minimal spatial
  changes)
• Priority on object categories (objects are mapped
  within same categories)

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SIS1 for Object tracing with priority on spatial
changes

• permutation list all possible mappings
• eliminationconcentration choose the mapping
  with the minimal spatial changes

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SIS2 for Object tracing with priority on object
category

• permutation list all possible mappings
• elimination remove mappings of different object
  categories
• eliminationconcentration choose the mapping
  within minimal spatial changes

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Why fast changing leads to illustiion?

• Conjection SIS2 takes more time than SIS1 in
  object mapping between scenes.

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Conclusion

• SIS shall be a Cognitive Architecture
• SIS for spatial knowldge acquisition within a
  scene
• SIS for spatial knowledge acquisition between
  scenes
• SIS for Spatial Cognition
• Spatial Cognition is foundamental to Human
  Intelligence
• SIS as a Cognitive Architecture for Human
  Intelligence

32
Outlooks

• Relations between SIS and other Cognitive
  Architectures, e.g. ACT-R, CLARION, ...
• Any difference to acquisit implicit knowledge and
  explicit knowledge