I like this work too

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في ١٦‏/٠٥‏/٢٠١٣، الساعة ٦:٣٣ م، كتب "Boris Kazachenko" <[email protected]>:

> Argh...
>  
> 
> From: Piaget Modeler
> Sent: Wednesday, May 15, 2013 11:59 PM
> To: AGI
> Subject: RE: [agi] Goal Selection
> 
> Well, perhaps you can diagram how you propose to implement it.
> I hope you will have a working implementation.
> ~PM
> 
> From: [email protected]
> To: [email protected]
> Subject: Re: [agi] Goal Selection
> Date: Wed, 15 May 2013 17:43:37 -0400
> 
> Once again, I work on the level of operations,  not some immutable "programs".
> Level-specific comparison-evaluations convert a queue of input patterns into 
> a queue of template patterns, which make-up a level.
> These patterns have level-specific syntax, incremented by corresponding 
> comparison-evaluations from the syntax of input patterns. 
> Levels "interact" with each other via feedforward & feedback.
> My whole intro is an attempt to explain how  this increase in complexity of 
> comparison-evaluations  *begins*.
> It's a 1D sequence, 2D diagram is a wrong format for such subject.
> I can't explain it better unless you tell me what in my intro is not clear to 
> you, Michael.
>      
> 
> From: Piaget Modeler
> Sent: Wednesday, May 15, 2013 5:14 PM
> To: AGI
> Subject: RE: [agi] Goal Selection
> 
> Boris,
> 
> What specific programs run to create the levels of structures? 
> 
> Can you diagram and label those programs? 
> Do the programs interact with one another? 
> 
> Kindly advise.
> 
> ~PM
> 
> 
> From: [email protected]
> To: [email protected]
> Subject: Re: [agi] Goal Selection
> Date: Wed, 15 May 2013 16:46:20 -0400
> 
>  
> The "architecture" is incremental: no blocks, just levels.
> All I have is incremental-complexity  comparison-evaluation operations, 
> explained in the intro.
> Everything else is learned, that's what makes it *general*.
> 
>  
> From: Piaget Modeler
> Sent: Wednesday, May 15, 2013 1:15 PM
> To: AGI
> Subject: RE: [agi] Goal Selection
> 
> Perhaps architecture diagram would be a more specific term.
> 
> What architecture implements your proposed system.  Can you draw it? 
> 
> Thanks,
> 
> ~PM
> 
> From: [email protected]
> To: [email protected]
> Subject: Re: [agi] Goal Selection
> Date: Wed, 15 May 2013 09:51:48 -0400
> 
> Michael,
>  
> > Do you have a diagram to go along with your explanation?
>  
> Depends what you're looking for. In humans,  every part of the brain affects 
> motivation & attention.
> For GI, the only useful motive is curiosity (a drive to maximize predictive 
> correspondence), &
> "discrete components" are learned patterns, rather than built-in "modules".
>  
> The nearest thing to a diagram that I have is grouping levels of search by 
> incremental order of distance
> between comparands:
> - Comparison of adjacent inputs, forming continuous patterns of incremental 
> dimensionality (distance = 1): 
> line segments: 1D, blobs: 2D, objects: 3D, & processes: 4D. This is similar 
> to connected-component analysis.
> - Cross-comparison across a whole queue of inputs, forming discontinuous 
> patterns per input.(distance->n).
> These patterns are fuzzy, & their overlap is compensated by selection among 
> inputs to a next level.
> - Comparison across a hierarchy of short-cuts to higher-level queues, 
> generated by feedback (distance -> nn)...:
> www.cognitivealgorithm.info.
> 
> > I'm looking at attention rather than motivation.
> > For me, attention is the filtering or re-prioritization of goals.
>  
> Attention is prioritizing areas of search, according to the weights assigned 
> by combined motives (salience).
> Conscious attention is WTA mechanism, imposed by our brain-to-body bottleneck:
> http://cognitive-focus.blogspot.com/2012/06/temporal-attention-span-our-dominant.html
> This is irrelevant for GI because it shouldn't assume any fixed "body".
> What's relevant is distributed "unconscious" attention, which is a 
> market-like mechanism that allocates
> cognitive resources to the areas of search, in proportion to their projected 
> contribution to total
> predictive correspondence of one's model of the environment.
>  
> > In the PAM-P2 system I have an intuition that a higher level of selection 
> > occurs than is explained than by basic action selection.
>  
> Right, this is a selection of "cognitive actions": prioritization of internal 
> search, covered above.
> 
> > Motivation is handled in PAM-P2 through the use of homeostatic variables 
> > and "urges",
> > deltas between current and target homeostatic variable values.
>  
> That's an equivalent of my "instincts", - a supervised learning part, 
> irrelevant for GI per se.
>  
> > My intuition tells me that there should be another, higher level of goal 
> > selection. (Or, perhaps goal
> filtering, not exactly sure). Something that operates above action selection 
> that takes into account
> all the possible goals the system could have and ensures that the most 
> important at the current
> moment are part of the agenda.
>  
> Again, you're talking about the same thing from different POVs.
> "Goal" is a positive-value-charged state, or its internalized representation.
> Motivation is what does this value-charging, thus determines the priority of 
> searching though related internal representations & external sources.  
>  
> 
> From: Piaget Modeler
> Sent: Tuesday, May 14, 2013 5:56 PM
> To: AGI
> Subject: RE: [agi] Goal Selection
> 
> Hi Boris, 
> 
> Thanks for the references.  Do you have a diagram to go along with your 
> explanation? 
> That would be much appreciated.  A diagram helps the explanatory cloud to be 
> decomposed 
> into discrete components.
> 
> I'm looking at attention rather than motivation.  Motivation is handled in 
> PAM-P2 through the 
> use of homeostatic variables and "urges", deltas between current and target 
> homeostatic variable 
> values.  For me, attention is the filtering or re-prioritization of goals.  
> In the PAM-P2 system I have an 
> intuition that a higher level of selection occurs than is explained than by 
> basic action selection.
> 
> In PAM-P2 there are two action selectors: the Reactor, which matches existing 
> solutions 
> to sensory stimuli, and the Deliberator which matches existing solutions with 
> active situations 
> and needs (goals).  Both action selectors operate in a case based manner, 
> where "solutions"
> are the cases.  Once a solution is selected, it may generate subgoals to 
> assist in attaining the
> overall solution. 
> 
> My intuition tells me that there should be another, higher level of goal 
> selection. (Or, perhaps goal
> filtering, not exactly sure). Something that  operates above action selection 
> that takes into account 
> all the possible goals the system could have and ensures that the most 
> important at the current 
> moment are part of the agenda.  
>  
> Your thoughts?
> 
> ~PM
> 
> From: [email protected]
> To: [email protected]
> Subject: Re: [agi] Goal Selection
> Date: Tue, 14 May 2013 17:15:51 -0400
> 
> You're really trying to understand how human  motivation works. I already 
> posted this, but in case you missed:
>  
> Human motivation: developmental perspective.
> 
> 
> Motivation is all mental mechanisms that drive our behavior, in which I 
> include cognitive behavior: analysis, introspection, & planning for somatic 
> behavior. 
> Values / motives in humans & higher animals can be divided into three broad 
> categories, according to the mechanism that formed or selected them:
> 
> Evolution selects instincts fit for their own propagation, innate but 
> subsequently modulated by usage, 
> Conditioning value-charges stimuli coincident with previously value-loaded 
> stimuli in time or space, 
> Cognitive curiosity searches / selects for predictive patterns, even if they 
> consist of value-free stimuli.
> 
> Higher mechanisms accelerate adaptive value acquisition by acting on 
> increasingly mediated responses: from immediate behavioral reactions to 
> longer-term attention, prediction, & planning.
> Brain areas that implement these value-acquisition mechanisms likely evolved 
> in the same sequence:
> 
> Instincts, largely physiological & traceable to 4Fs, are encoded mainly in 
> brainstem & hypothalamus. 
> Conditioning is initiated by basal ganglia & limbic system, then extended & 
> generalized by neocortex. 
> Predictive curiosity is an innate driver of neocortex, which is also heavily 
> modulated by lower motives.
> 
> This scheme is vaguely similar to triune brain model, but in my 
> interpretation these substrates differ mainly in the mechanism by which they 
> acquire values, rather than in resulting & relatively transient motives 
> themselves. These value acquisition mechanisms are innate, but their relative 
> strength varies.
> 
> Our instincts are pretty basic & similar to those of other mammals. An 
> excellent account of that level of motivation is Jaak Panksepp‘s “Archaeology 
> of Mind: Neuroevolutionary Origins of Human Emotions“. The discussion below 
> is mostly on conditioning & cognition: increasingly adaptive mechanisms which 
> seem to strengthen with our personal growth:
>  
> http://cognitive-focus.blogspot.com/2012/06/motivation-evolution-of-value.html
> 
>  
> 
> From: Piaget Modeler
> Sent: Tuesday, May 14, 2013 4:17 PM
> To: AGI
> Subject: RE: [agi] Goal Selection
> 
> Getting Closer:
> 
> Top-down versus bottom up attentional control: a failed theoretical dichotomy
> 
> http://ems.psy.vu.nl/userpages/theeuwes/Trends_2012_Awh.pdf
> 
> The priority map notion is closer to what I was looking for.  
> I know that priorities fit in somehow.
> 
> ~PM
> 
> 
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