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Modeling Human Intelligence as a Slow Intelligence System

Tiansi Dong Department of Computer Science University of Hagen Germany. Modeling Human Intelligence as a Slow Intelligence System. Outline. Slow Intelligence System (SIS) Properties of Human Intelligence The question Case study in Spatial Reasoning Within one snapshot view

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Modeling Human Intelligence as a Slow Intelligence System

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  1. Tiansi Dong Department of Computer Science University of Hagen Germany Modeling Human Intelligence as a Slow Intelligence System

  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

  6. Properties of Human Intelligence Babies cannot see constant objects

  7. Properties of Human Intelligence Now suppose it not about apple, rather football money bus Spatial cognition is foundamental

  8. 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

  10. SIS for Spatial Knowledge within a Scene • Object categories • A picture • A lady • A wall • Spatial relations • On, in • Back, left • Near

  11. Specific Question • Object categories • A picture • A lady • A wall • Cross linguistic spatial relations • in, on, near, front, left,... • 上，左，前 ?

  12. 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 RCC–From RCC to RCC++, Journal of Philosophical Logic • Spatial model is region-based • 'connection' is primitive

  14. 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)]

  16. 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

  17. 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

  18. 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

  19. Spatial Reasoning for orientation • Orientation is determined by the shape of the reference object, and the method of distance comparison. NE N NW Reference Object W E SW S SE

  20. 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-iθ The orientation of P can be defined as the point on the unit circle which is nearest to P. Q: e-iθ W θ 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)

  23. SIS for Spatial Reasoning between snapshots:Object tracing

  24. SIS for Spatial Reasoning between snapshots:Object tracing

  25. SIS for Spatial Reasoning between snapshots:Object tracing

  26. SIS for Spatial Reasoning between scene:Object tracing • Fast changining leads to an illusion bird → rabbit, rabbit → bird • Otherwise, bird flies, rabbit moves

  27. SIS for Spatial Reasoning between scene:Object 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)

  28. SIS1 for Object tracing with priority on spatial changes • [permutation] list all possible mappings • [elimination+concentration] choose the mapping with the minimal spatial changes

  29. SIS2 for Object tracing with priority on object category • [permutation] list all possible mappings • [elimination] remove mappings of different object categories • [elimination+concentration] choose the mapping within minimal spatial changes

  30. Why fast changing leads to illustiion? • Conjection: SIS2 takes more time than SIS1 in object mapping between scenes.

  31. 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

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