Richard,
I'll try to summarize my solutions to these problems which allow to
use a network without need for explicit copying of instances (or any
other kind of explicit allocation of entities which are to correspond
to instances). (Although my model also requires ubiquitous induction
between nodes which disregards network structure.)
Basic structure of network: network is 'spiking' in the sense that it
operates in real time and links between nodes have a delay. Input
nodes send in the network sensory data, output nodes read actions. All
links between nodes can shift over time and experience through
induction. Initial configuration specifies simple pathways from input
to output, shifting of links changes these pathways, making them more
intricate to reflect experience.
Scene (as a graph which describes objects) is represented by active
nodes: node being active corresponds to feature being included in the
scene. Not all features present in the scene are active at the same
time, some of them can activate periodically, every several tacts or
more, and some other features can be represented by summarizing
simplified features (node 'apple' instead of 3D sketch of its
surface).
Network edges (links) activate the nodes. If condition (configuration
of nodes from which link originates) for a link is satisfied, and link
is active, it activates the target node.
Activation in the network follows a variation of Hebbian rule,
'induction rule' (which is essential for mechanism of instance
representation): link becomes active (starts to activate its target
node) only if it observed that node to be activated after condition
for link was satisfied in a majority of cases (like 90% or more). So,
if some node is activated in a network, there are good reasons for
that, no blind association-seeking.
Representation of instances. If scene contains multiple instances of
the same object (or pattern, say an apple), and these patterns are not
modified in it, there is no point in representing those instances
separately: all places at which instances are located ('instantiation
points', say places where apples lie or hang) refer to the same
pattern. The only problem is modification of instances in specific
instantiation points.
This scene can be implemented by creating links from instantiation
points to nodes that represent the pattern. As a result, during
activation cycle of represented scene, activation of instantiation
points leads to activation of patterns (as there's only one pattern
for each instantiation point, so induction rule works in this
direction), but not in other direction (as there are many
instantiation points for the pattern, none of them will be a target of
a link originating from the pattern).
This one-way activation results in a propagation of 'activation waves'
from instantiation points to the pattern, so that each wave 'outlines'
both pattern and instantiation point. These waves effectively
represent instances. If there's a modifier associated with specific
instantiation point, during an activation wave it will activate during
the same wave as pattern does, and as a result it can be applied to
it. As other instantiation points refer to the pattern 'by value',
pattern at those points won't change much.
Also, this way of representing instances is central to extraction of
similarities: if several objects are similar, they will share some of
their nodes and as a result their structures will influence one
another, creating a pressure to extract a common pattern.
Creation of new nodes. Each new node during a creation phase
corresponds to an existing node ('original node') in the network.
During this phase (which isn't long), each activated link that
connects to original node (both incoming and outgoing connections) is
copied so that in a copy original node is substituted by a new node.
As a result, new node will be active in situations in this original
node activated during creation of the new node. New node can represent
episodic memory or more specific subcategory of category represented
by original node. Initially, new node doesn't influence behavior of
the system (as it's activated in a subset of tacts in which original
node can activate), but because of this difference it can obtain
inductive links different from those that fit original node.
On Dec 5, 2007 4:47 AM, Richard Loosemore <[EMAIL PROTECTED]> wrote:
> Dennis Gorelik wrote:
> > Richard,
> >
> >> 3) A way to represent things - and in particular, uncertainty - without
> >> getting buried up to the eyeballs in (e.g.) temporal logics that nobody
> >> believes in.
> >
> > Conceptually the way of representing things is described very well.
> > It's Neural Network -- set of nodes (concepts), when every node can be
> > connected with the set of other nodes. Every connection has it's own
> > weight.
> >
> > Some nodes are connected with external devices.
> > For example, one node can be connected with one word in text
> > dictionary (that is an external device).
> >
> >
> > Do you see any problems with such architecture?
>
> Many, unfortunately.
>
> Too many to list all of them. A couple are: you need special extra
> mechanisms to handle the difference between generic nodes and instance
> nodes (in a basic neural net there is no distinction between these two,
> so the system cannot represent even the most basic of situations), and
> you need extra mechanisms to handle the dynamic creation/assignment of
> new nodes, because new things are being experienced all the time.
>
> These extra mechanisms are so important that is arguable that the
> behavior of the system is dominated by *them*, not by the mere fact that
> the design started out as a neural net.
>
> Having said that, I believe in neural nets as a good conceptual starting
> point.
>
> It is just that you need to figure out all that machinery - and no one
> has, so there is a "representation" problem in my previous list of problems.
>
>
>
>
> Richard Loosemore
>
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