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Cognitive Robotics
Level of Abstraction
Case Study- Suspension Bridge
Case Study- Suspension Bridge
Case Study- Suspension Bridge
Higher Level Abstraction
To find out if they are strong enough to bear the required loads with
 an acceptable level of movement,
 typically as a function of different patterns of traffic flow,
 wind conditions, and
 tidal forces.
Lower Level Abstraction
It is used to check the materials used for it and the interdependency
 the concrete foundations,
 the suspension cables,
 the cable anchors,
 the road surface, and
 the traffic that uses it
Abstraction in Cognitive System
 Deciding on the best level of abstraction is not always straightforward. Other types of
system — biological ones.
 For biological systems level of abstraction is very person to person and there is some
disagreement in the scientific community.
 Two approaches are followed for abstracting a cognitive system
 Marrs Model
 Scott Kelso’s Model
Three Level of Abstraction
 Also known as Levels of Understanding
developed by David Marr for human visual
system.
 At the level of the computational theory, you
need to answer questions such as
 Goal of computation
 Why is it appropriate?
 How to implement it through logic and
strategy?
 The Second level Represents
 How the computation theory need to be applied?
 Representation of input and output
 Required algorithm to input transform input to output
Three Level of Abstraction
 At the level of the Hardware Implementation
following things are asked:
 How to build the physical system?
 How the representation and algorithms
physically realized?
How to see three level of Abstraction?
 According to Marr these levels are loosely
coupled – think about only one level rather
concentrating on lower level.
 The first level represents the problem through
some mathematical formalism and then
moving on to representations and algorithms
once the model is complete.
 The algorithm and representation levels are more accessible, it is the computational or
theoretical level that is critically important from an information processing
perspective.
 The states that the problem can and should first be modelled at the abstract level of the
computational theory without strong reference to the lower and less abstract levels
Conclusion of Marrs Model
 Many people believe that cognitive systems — both biological and artificial — are
effectively information processors, Marr’s hierarchy of abstraction is very useful.
 According to Marr
“Trying to understand perception by studying only neurons is
like trying to understand bird flight by studying only feathers: it
just cannot be done. In order to understand bird flight, we have to
understand aerodynamics; only then do the structure of feathers
and the different shapes of birds’ wings make sense”
 First decouple the different levels of abstraction and begin your analysis at the
highest level and avoid the implementation issues until the computational or
theoretical model is complete
Scott Kelso’s Model
 He think that the physical implementation has a direct role to play in understanding the
computational theory
 He takes the example of non-linear dynamical types
of systems that he believes may provide the true
basis for cognition and brain dynamics.
 All the level of abstraction should developed
distinctly but at the same time.
 Boundary – determines the goal of the system
 Collective Variables - characterizes the behavior of
the system.
 Components - related to the Physical System
Scott Kelso’s Model
 The specification of these three levels of model
abstraction are tightly coupled and mutually
dependent.
 The environment constraints decides the behavior
of the system and do the feasible study.
 At the same time properties of physical system may
simplify the necessary behavior.
 According to Rolf Pfeifer the properties of the physical shape or the forced needed
for required movements may actually simplify the computational problem.
 The realization of the system and its particular shape or morphology cannot be
ignored and should not be abstracted away when modelling the system.
Relation between system realization and modelling
 The specification of these three levels of model
abstraction are tightly coupled and mutually
dependent.
 The environment constraints decides the behavior
of the system and do the feasible study.
 At the same time properties of physical system may
simplify the necessary behavior.
 If we look carefully, we see a circularity, with
everything depending on something else. It’s not
easy to see how you break into the modelling circle.
Modified Marr’s Model
Consider these questions:
a) Is there a computational
theory for learning?
b) Are there algorithms for
learning?
c) Are there mechanisms in
the neuroanatomy for
learning?
Answer is yes. I think it might
be more useful to think of
learning as having a
computational, algorithmic
and mechanisms level.

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5.levelofAbstraction.pptx

  • 4. Case Study- Suspension Bridge Higher Level Abstraction To find out if they are strong enough to bear the required loads with  an acceptable level of movement,  typically as a function of different patterns of traffic flow,  wind conditions, and  tidal forces. Lower Level Abstraction It is used to check the materials used for it and the interdependency  the concrete foundations,  the suspension cables,  the cable anchors,  the road surface, and  the traffic that uses it
  • 5. Abstraction in Cognitive System  Deciding on the best level of abstraction is not always straightforward. Other types of system — biological ones.  For biological systems level of abstraction is very person to person and there is some disagreement in the scientific community.  Two approaches are followed for abstracting a cognitive system  Marrs Model  Scott Kelso’s Model
  • 6. Three Level of Abstraction  Also known as Levels of Understanding developed by David Marr for human visual system.  At the level of the computational theory, you need to answer questions such as  Goal of computation  Why is it appropriate?  How to implement it through logic and strategy?  The Second level Represents  How the computation theory need to be applied?  Representation of input and output  Required algorithm to input transform input to output
  • 7. Three Level of Abstraction  At the level of the Hardware Implementation following things are asked:  How to build the physical system?  How the representation and algorithms physically realized?
  • 8. How to see three level of Abstraction?  According to Marr these levels are loosely coupled – think about only one level rather concentrating on lower level.  The first level represents the problem through some mathematical formalism and then moving on to representations and algorithms once the model is complete.  The algorithm and representation levels are more accessible, it is the computational or theoretical level that is critically important from an information processing perspective.  The states that the problem can and should first be modelled at the abstract level of the computational theory without strong reference to the lower and less abstract levels
  • 9. Conclusion of Marrs Model  Many people believe that cognitive systems — both biological and artificial — are effectively information processors, Marr’s hierarchy of abstraction is very useful.  According to Marr “Trying to understand perception by studying only neurons is like trying to understand bird flight by studying only feathers: it just cannot be done. In order to understand bird flight, we have to understand aerodynamics; only then do the structure of feathers and the different shapes of birds’ wings make sense”  First decouple the different levels of abstraction and begin your analysis at the highest level and avoid the implementation issues until the computational or theoretical model is complete
  • 10. Scott Kelso’s Model  He think that the physical implementation has a direct role to play in understanding the computational theory  He takes the example of non-linear dynamical types of systems that he believes may provide the true basis for cognition and brain dynamics.  All the level of abstraction should developed distinctly but at the same time.  Boundary – determines the goal of the system  Collective Variables - characterizes the behavior of the system.  Components - related to the Physical System
  • 11. Scott Kelso’s Model  The specification of these three levels of model abstraction are tightly coupled and mutually dependent.  The environment constraints decides the behavior of the system and do the feasible study.  At the same time properties of physical system may simplify the necessary behavior.  According to Rolf Pfeifer the properties of the physical shape or the forced needed for required movements may actually simplify the computational problem.  The realization of the system and its particular shape or morphology cannot be ignored and should not be abstracted away when modelling the system.
  • 12. Relation between system realization and modelling  The specification of these three levels of model abstraction are tightly coupled and mutually dependent.  The environment constraints decides the behavior of the system and do the feasible study.  At the same time properties of physical system may simplify the necessary behavior.  If we look carefully, we see a circularity, with everything depending on something else. It’s not easy to see how you break into the modelling circle.
  • 13. Modified Marr’s Model Consider these questions: a) Is there a computational theory for learning? b) Are there algorithms for learning? c) Are there mechanisms in the neuroanatomy for learning? Answer is yes. I think it might be more useful to think of learning as having a computational, algorithmic and mechanisms level.

Editor's Notes

  1. Identified by David Marr
  2. Identified by David Marr
  3. Identified by David Marr
  4. Identified by David Marr
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  7. Identified by David Marr
  8. Identified by David Marr