Well, in the language I normally use to discuss AI planning, this
would mean that

1)keeping charged is a supergoal

2)
The system knows (via hard-coding or learning) that

finding the recharging socket ==> keeping charged

(i.e. that the former may be considered a subgoal of the latter)

3)
The system then may construct various plans for "find the recharging
socket", and these plans may involve creating various subgoals of this
subgoal

-- Ben

On 11/22/06, Bob Mottram <[EMAIL PROTECTED]> wrote:

Things like finding recharging sockets are really more complex goals built
on top of more primitive systems.  For example, if a robot heading for a
recharging socket loses a wheel its goals should change from feeding to
calling for help.  If it cannot recognise a deviation from the "normal"
state then it will fail to handle the situation intelligently.  Of course
these things can be "hard coded", but hard coding isn't usually a good
strategy, other than as initial scaffolding.  Systems which are not
adaptable are usually narrow and brittle.



On 22/11/06, Charles D Hixson <[EMAIL PROTECTED]> wrote:
> I don't know that I'd consider that an example of an uncomplicated
> goal.  That seems to me much more complicated than simple responses to
> sensory inputs.   Valuable, yes, and even vital for any significant
> intelligence, but definitely not at the minimal level of complexity.
>
> An example of a minimal goal might be "to cause an extended period of
> inter-entity communication", or "to find a recharging socket".  Note
> that the second one would probably need to have a hard-coded solution
> available before the entity was able to start any independent
> explorations.  This doesn't mean that as new answers were constructed
> the original might not decrease in significance and eventually be
> garbage collected.  It means that it would need to be there as a
> pre-written answer on the tabula rasa.  (I.e., the tablet can't really
> be blank.  You need to start somewhere, even if you leave and never
> return.)  For the first example, I was thinking of "peek-a-boo".
>
> Bob Mottram wrote:
> >
> > Goals don't necessarily need to be complex or even explicitly
> > defined.  One "goal" might just be to minimise the difference between
> > experiences (whether real or simulated) and expectations.  In this way
> > the system learns what a normal state of being is, and detect
deviations.
> >
> >
> >
> > On 21/11/06, *Charles D Hixson* <[EMAIL PROTECTED]
> > <mailto:[EMAIL PROTECTED]>> wrote:
> >
> >     Bob Mottram wrote:
> >     >
> >     >
> >     > On 17/11/06, *Charles D Hixson* < [EMAIL PROTECTED]
> >     <mailto:[EMAIL PROTECTED]>
> >     > <mailto: [EMAIL PROTECTED]
>
> >     <mailto:[EMAIL PROTECTED]>>> wrote:
> >     >
> >     >     A system understands a situation that it encounters if it
> >     predictably
> >     >     acts in such a way as to maximize the probability of
> >     achieving it's
> >     >     goals in that situation.
> >     >
> >     >
> >     >
> >     >
> >     > I'd say a system "understands" a situation when its internal
> >     modeling
> >     > of that situation closely approximates its main salient
> >     features, such
> >     > that the difference between expectation and reality is minimised.
> >     > What counts as salient depends upon goals.  So for example I
> >     could say
> >     > that I "understand" how to drive, even if I don't have any
detailed
> >     > knowledge of the workings of a car.
> >     >
> >     > When young animals play they're generating and tuning their
models,
> >     > trying to bring them in line with observations and goals.
> >     That sounds reasonable, but how are you determining the match of the
> >     internal modeling to the "main salient features".  I propose that
> >     you do
> >     this based on it's actions, and thus my definition.  I'll admit,
> >     however, that this still leaves the problem of how to observe what
> >     it's
> >     goals are, but I hypothesize that it will be much simpler to
> >     examine the
> >     goals in the code than to examine the internal model.
> >
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