MRI 2

MRI 2.1 Consolidation Attempt
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We've gotten many ideas from many places. Lets try to consolidate them and find a good integration of all of them.


First is a list and synopsis of the key ideas we have picked from others and also those we created:

From Stephen Wolfram in "A New Kind of Science"
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Complex Behaviour can arise from systems whose behaviour are based on simple rules. An example of such is Cellular Automata, which is composed of an array of elements whose behaviour is dictated by simple rules which are functions of the state of adjacent cells. This is reminiscent of neural networks, in which simple rules govern the firing of each neurone (at least this is so in their general behaviour)

An increase in the complexity of the rules does not lead to a corresponding increase in thhe complexity of the resulting behaviour of the system.

Perception and analysis is very much concerned with summarising the details of some raw input.
To accomplish this it is necessary to IDENTIFY PATTERNS, and COMPRESS DATA.

(pg. 627 paraphrased)
The power of human thought lies in its ability to store and quickly retreive a huge amount of information.

That allows us to make many connections between concepts. Making references to items in memory and forming new connections between them and new perceptions forms much of human thought.
(I ask, "what else is there to human thought?")


From the Connectionist Model of Cognition
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Concepts are represented as patterns of activity in neural networks.

These are present in material form as the strengths and weights of the synaptic connections between neurones.

Hebb's Rule is the method by which the connection strengths change.

If objects/concepts are represented graphically as paths, then the points of intersection of paths represents the relations between the two things...
e.g. John is a boy is depicted by the 'john' path intersecting the 'boy path'. The intersection is manifested in neural networks by the synaptic connections, and the strength of the connections means the strength of the relation.

From Subsumption
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We borrow the concept of layers of behaviour and agents each with its own simple, low-level task. Thus the complex behaviour we want an intelligent system to manifest can be split into simple specific tasks handled by simple systems.


Misc.
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Motor control is achieved by huge networks of pattern generators whose pattern output control the contraction of muscles that give rise to


Some of my own early ideas before those stated in MRI - 1
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Basic Principles of a good control system:

Decentralised operation - everything is everywhere and nowhere...in no particular place.

The architecture breeds its own intelligence. Hence the complexity of its behaviour comes from the running of the system itself...design of the architecture is only to enable as much of this complexity to evolve from the unsupervised operation of the system

Precision is not always necessary. The system should be tolerant of inprecision

The system should be adaptive. It should learn and optimise itself. (Consult
MRI 1.2)